{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Создание лингвистических переменных\n",
    "\n",
    "Входные X: task и target\n",
    "Выходные Y: need"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from skfuzzy import control as ctrl\n",
    "\n",
    "task  = ctrl.Antecedent(np.arange(0, 100, 1), \"task\")\n",
    "target = ctrl.Antecedent(np.arange(0, 100, 1), \"target\")\n",
    "need = ctrl.Consequent(np.arange(0, 130, 1), \"need\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Формирование нечетких переменных для лингвистических переменных и их визуализация"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\TEMP_UNIVERSITY\\mai\\.venv\\Lib\\site-packages\\skfuzzy\\control\\fuzzyvariable.py:125: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n",
      "  fig.show()\n"
     ]
    },
    {
     "data": {
      "image/png": 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FpLgR4gJ0tSdJbilgg20Ey788rHYcITqK7g/hKTI11Yms4iwmJEyQKSkvJMWNEBdiyQR0TJxzHat3FPONpVztREJ8T6ezdyuW4qaD2uZadpbuZFbKLLWjuKWmpibuv/9+4uLiCAgIYNq0aezYsaPTaxsaGrjsssuYOnWqx0xVSXEjxIVYzJA0ihumjWRCvyiWfbifM83WCz9PCFcxmqA8G6rPcWyIF9p8YjOttlZm9ZmldhS39Nvf/pYPPviAf/zjH+zevZv+/fszb948KioqOlxXVVXFxRdfjM1mY926dURERKgTuJt81Q4ghFuz2ezt7cfejo+PjuXXD+eyP2/kxf/msOzywWqnE8Ku30xAZx9lHHOr2mncQlZxFoOiBpEYkujaGzc3QHmOa+8JEDMQ/IO6dGl9fT2vvPIKb775JpddZt8iv2rVKtatW8drr73G+PHjASgpKeHmm29mwIABvP322/j7+zstvqNJcSPE+ZTsg4bT353jA8bYEB6YM4Dn1mZz9agkhiaFqxxQCCAoCpLHgCVDihvsZ0ltOLaBWwbf4vqbl+fAqzNdf98710PSqC5darFYaGlpYerU7xda+/n5MWHCBA4fPtxe3Fx88cVMmDCBd999F71e74zUTiPFjRDnYzGDfwikTGx/6M4Z6Xy05zi//+wQ7945CZ1Op2JAIb5jNMGOv4PNCj6e9Y3I0faW7aW2uVad9TYxA+2Fhhr3dbArrriCDz74gEOHDjF8+HCHv74zSXEjxPlYzJA2HXy/H4710/vw2JVDWPD6dr7cX8IVI1w87C1EZ4xzYMP/wslv7aM4XiyzOJO4wDiGRA1x/c39g7o8gqIWo9GIv78/mzdvpm/fvgC0tLSwY8cOlixZ0n7dihUrCAkJYc6cOWRlZTFkiAp/nj0kC4qFOJfmentztE66Es8cGMvcwXE88+VhGltkcbFwA33GgX+o1++aUhSFrOIsZqbMlFHVcwgODubuu+/mN7/5DWvWrOHQoUPccccdNDQ0sGjRog7X/ulPf+KWW27BZDJx5MgRlRJ3nxQ3QpxLwSawtZzzyIVHrhhCWW0jqzbkuTiYEJ3Q+0G/GV5f3ORV51FcWyxbwC9gxYoV3HDDDdx6662MGTOG3Nxcvv76ayIjI8+69oUXXuCmm27CZDKRk6PCYukekOJGiHOxmCE8FaKNnf52v5hgFk7tx8tZFkqqG10cTohOGGdD8TZoqlU7iWqyirMI9A1kYuLEC17rzQICAvjLX/7CqVOnaGxsZNOmTe0LiWfNmoWiKB22ff/lL3/hxIkTDBzo+LU9ziDFjRDnYjHbv1mcZ2j7XlN/gvz1PLvGc4ZrhYYZTWBrtY86eqms4iymJE3BoDeoHUWoSIobITpTVWzf0nmBU8DDAvz4zbyL+GjPcXYVVroonBDnEJUOEX29dmrq9JnTfHvqW5mSElLcCNGpvEzQ+djXMFzAjeNSGJoUxpOfHcRmU1wQTohz0OnsBbmXFjcbjm0AYEafC/+7FdomxY0QnbGYIWmMvTnaBeh9dDxx1VC+PVbNZ/tOuCCcEOdhNMHpXKgsVDuJy208vpGRsSOJCrjwv1uhbVLcCPFjNivkZV1wSuqHJvSLYu7gOF5Yl0OL1ea8bEJcSL8Z9lHHvEy1k7iUTbGxo2QHk5ImqR1FuAEpboT4sZN74Uxlt4obgKUXX0TB6Qbe3yWHFwoVBUZA8jivm5qyVFmoaqpiXPw4taMINyDFjRA/ZjGDIczeFK0bhiSFcdXIJP6ScVQa+wl19Z9jH320ec/fwx0lO/Dz8WNE7Ai1owg3IMWNED9mybQP7ev9uv3UX80dQFltE29tK3JCMCG6yGiCxmo4sUftJC6zs3Qnw2OGE+gbqHYU4QakuBHih5pq7U3QjLN79PT02BB+MqYPL2fmUt/U6uBwQnRR0hgwhHvN1JSiKOws2cm4BJmSEnZS3AjxQwWb7E3Qurne5ofunzuA2sZW3tic78BgQnSD3hfSvecoBkuVhcqmSsYnjFc7inATUtwI8UO5GRCZZm+G1kPJEYH8bGIq/7chj+qGFsdlE6I7jCYo3m6fntK4HaU78PXxZWTsSLWjCDchxY0QP2Qx92rUps3i2f1ptSr83waLA0IJ0QNGEyhWyN+gdhKn21GygxExI2S9jRtpbm5W9f5S3AjRprIAKiwOKW5iQw0snJrGG5sLOFXb1PtsQnRX2wikRdv9bhRFYVfpLsbGj1U7iseYNWsW9913H0uWLCEyMpL4+HhWrVpFfX09CxcuJDQ0lP79+/PVV1+1P2f9+vVMmDABg8FAYmIiDz/8MK2trR1e895772XJkiXExMQwb948AA4cOMBll11GSEgI8fHx3HrrrZSXlzv9c5TiRog2lkzQ6SFtukNe7v/NMKL30fHaJll7I1TiBUcx5FXnUdFYIettuukf//gHMTExbN++nfvuu4+7776bG2+8kSlTprB7924uueQSbr31VhoaGjh+/DiXX34548eP59tvv+WVV17htdde4+mnnz7rNf39/dm8eTMrV66kqqoKk8nE6NGj2blzJ2vWrKG0tJSbbrrJ6Z+fTlEUrzoMp6amhvDwcKqrqwkLC1M7jnAn794KtSXwy3UOe8ln1xzhX1sK2fyQifCg7m8tF6JXjnwBq38G9+/p1Toyd7b6yGqe3fEsm3+6mSC/IJfdt6WlhVOnThEbG4uf3/f/ts+0niG/2vU/0PQL79flablZs2ZhtVrZuHEjAFarlfDwcK6//nr++c9/AlBSUkJiYiJbtmzhs88+44MPPuDw4cPodDoAXn75ZR566CGqq6vx8fFh1qxZ1NTUsHv37vb7PP3002zcuJGvv/66/bFjx46RkpJCdnY2AwcOPCvbuf5cu8u3x88UQkusrZC/Hibd49CX/cXUfry+KZ9/bingvjkDHPraQlxQ2nTw8bWPSmq0uNlRsoNh0cNcWticT351Pjd/frPL7/vule8yJHpIl68fMeL7Zod6vZ7o6GiGDx/e/lh8fDwAZWVlHD58mMmTJ7cXNgBTp06lrq6OY8eOkZqaCsDYsR2nBr/99lsyMzMJCQk56/4Wi6XT4sZRpLgRAuzNzhqrwTjHoS8bG2rg5vEpvL45n0XT+xHkL//khAsFhEGfCfapqfGL1E7jcIqisLN0JzcMuEHtKO36hffj3SvfVeW+3fHjURGdTtfhsbZCxmbr+ll5wcHBHX5dV1fHVVddxbPPPnvWtYmJid2J223ylVYIsH/xDwiHpNEOf+k7Z6Tz9rYi3tlezKJp3fsCJESvGU3wzV/so5N6bX3Jz6/Op6Kxwq2a9wX6BnZrBMUTDB48mA8++ABFUdqLns2bNxMaGkqfPn3O+bwxY8bwwQcfkJaWhq+va//uyYJiIcBe3PSb6ZQv/n0ig7hmVDKrNuTR1Oo9Z/0IN2E0QVMNHN+ldhKH21GyA1+dL6NiR6kdRdPuueceiouLue+++zhy5AiffPIJTzzxBEuXLsXH59xlxOLFi6moqGD+/Pns2LEDi8XC119/zcKFC7Fanfu1UIobIRqr4dgOh2wBP5e7Z6VTWtvIR7uPO+0eQnQqaRQERGhy19SO0h0MjRnqNutttCo5OZkvv/yS7du3M3LkSO666y4WLVrEo48+et7nJSUlsXnzZqxWK5dccgnDhw9nyZIlREREnLcocgTZLSXE4c/h3VvggW/tvUGc5O5/7+LQyRoyls7EVy8/VwgX+s8CqDkOv/yv2kkcRlEUZv1nFtcPuJ4Hxjzg8vs7aleP6MhRf67yFVYISwZEGZ1a2ADcM6s/hacb+PJAiVPvI8RZjCb7tNSZSrWTOEx+jX29zfh46W8jzibFjRAOOnLhQob3CWfGwFhezszFywZMhdqMs0Gxaeoohp0lO9Hr9IyKG6V2FOGGpLgR3q0iz37sgguKG4DFs4wcKaklM7vMJfcTAoCIVIgeoKl1NztLdjI0WtbbiM5JcSO8m8Vsb3KWNs0lt5vQL4qRfcJ5fVOBS+4nRDujCXLNoJFRw33l+xgZJ6eAi85JcSO8myXT3uQswDWLy3U6Hb+Y1o9NueVkl9S65J5CAPbiprrIPlrp4SoaKzhed5zhMcMvfLGTyRSzYznqz1OKG+G9rC32NQgumpJqc/nwROLDDLyxWQ7UFC6UNg18/DQxNXWg/AAAw2KGqZZBr9cD0NzcrFoGLWrrf9PbreKqFzcvvfQSaWlpBAQEMHHiRLZv337e61988UUuuugiAgMDSUlJ4Ve/+hWNjY0uSis05fgue3Oz/q4tbvz0Ptw2OY0P9xzndF2TS+8tvJghBFImaqK4OVh+kAhDBH1Czt0d19l8fHwICgqipqaGuro6mpubaWlpkY9efDQ3N1NTU4O/v3+vixtVe3G/++67LF26lJUrVzJx4kRefPFF5s2bR3Z2NnFxcWdd//bbb/Pwww/z+uuvM2XKFHJycrj99tvR6XQ8//zzKnwGwqNZzBAYCYmjXH7rn01I5S8ZR3lnexH3muRATeEi/U2w8Xn7qKXec3uz7C/fz7CYYR0OclRDeHg4YO+fJhxDp9MRHR3d6/dW1eLm+eef54477mDhwoUArFy5ki+++ILXX3+dhx9++Kzrv/nmG6ZOncrPfvYzANLS0pg/fz7btm1zaW6hERYzpM8CH73Lbx0Z7M/1Y/rwzy2F3DnDiL+v6oOowhsYTZDxpL0jd98paqfpEUVROFB+gJ8O+qnaUdDpdERERBAWFub04wS8ha+vr0OKVtWKm+bmZnbt2sWyZcvaH/Px8WHu3Lls2bKl0+dMmTKFf//732zfvp0JEyaQl5fHl19+ya233nrO+zQ1NdHU9P3Qv1TYArA3Mzu+C8bcplqEX0xN453tRXyx/wTXjVZveF14kYSREBgFuRkeW9wcrztOZVOlquttfszHx8fpxwmI7lHt3SgvL8dqtRIfH9/h8fj4eEpKOu/g+rOf/Ywnn3ySadOm4efnh9FoZNasWfzud787532WL19OeHh4+0dKSopDPw/hofLW25uapc9WLcKA+FBmDIzltU35suNCuIaPj72hnwevu3GHxcTC/XlUqZmVlcUzzzzDyy+/zO7du/nwww/54osveOqpp875nGXLllFdXd3+UVxc7MLEwm1ZzPamZhHqFru/mJrGgeM17CzUTlt84eaMJjixBxoq1E7SI/vL95MckkxUQJTaUYQbU21aKiYmBr1eT2lpaYfHS0tLSUhI6PQ5jz32GLfeeiu//OUvARg+fDj19fXceeedPPLII50OCxoMBgwGg+M/AeG5FMXe32bQ5WonYcaAWIyxwby+KZ/xafLFWrhA+mxAgbwsGHa92mm67UD5ARm1ERek2siNv78/Y8eOJSMjo/0xm81GRkYGkydP7vQ5DQ0NZxUwbb0GZFhfdNlpi72ZmYv723TGx0fHwqn9+PpgCcUVDWrHEd4gPBliB3nk1FSrrZVDpw+5RfM+4d5UnZZaunQpq1at4h//+AeHDx/m7rvvpr6+vn331G233dZhwfFVV13FK6+8wurVq8nPz2fdunU89thjXHXVVe1FjhAXZDHbm5n1nap2EgBuGNOHEIMvb28vUjuK8BZGk3300sN+KLRUWWi0NsrIjbggVbeC33zzzZw6dYrHH3+ckpISRo0axZo1a9oXGRcVFXUYqXn00UfR6XQ8+uijHD9+nNjYWK666ir+8Ic/qPUpCE9kMUPqJHtTMzcQ6K/n+jF9eG9nMb+aO1C2hQvnM5pg68tQfhRiB6qdpsv2l+/HR+fD4KjBakcRbk6neNl8Tk1NDeHh4VRXVxMW5przhIQbaW2GP/aD6b+G6UvVTtPuaGktF7+wgb/9bDRXjkhSO47QuuYGeLYvXPwUTLpL7TRd9j/f/A/7y/fzwdUfqB1FuDn5EVF4l2M7oLnOLdbb/NCA+FAm9Ivira0yNSVcwD8IUid73Lqb/eX7Zb2N6BIpboR3sZghKBoSRqid5Cy3TExlS95pcsvq1I4ivIHRBAUbodUzzjdraGkgtypX1tuILpHiRngXS4Z9K6wbdhO9dFgCUcH+vCMLi4UrGE3Q0gDFnnF8zeGKw9gUm4zciC5xv6/wQjhL/Wk4sdftpqTaGHz13Di2D+/vOkZji5xTI5wsfhgEx3rM1NSB8gME6AMwRhjVjiI8gBQ3wnvkZwGKvf28m5o/IZXqMy18se+k2lGE1vn42EcxPai4GRI9BF8fVTf5Cg8hxY3wHhYzxA6GMPfdjZQWE8y0/jG8ta1Q7SjCGxhNcPJbqC9XO8kF7S/fL+ttRJdJcSO8Q9uRC246JfVDt0xMZXdRFYdOyAn2wsnaRjHzslSNcSEVjRUcrzsu621El0lxI7xDeQ7UHIf+7l/czB0ST2yogbe3y+iNcLLQBIgb6vZTU3ISuOguKW6Ed7CYQW+A1ClqJ7kgP70PPx2fwsd7TlDf1Kp2HKF1/U32fx9u3M/1QPkBIg2RJIckqx1FeAgpboR3sJih72R78zIP8NMJqdQ3t/LZtyfUjiK0zmiC2pNw6ojaSc7pQPkBhsQMQafTqR1FeAgpboT2tTZBwSaPWG/TJjkikGn9Y3h/1zG1owitS50MvgGQm6F2knPKrsyW86REt0hxI7SvaKu9WZkHFTcAPxnbh52FleSdko7Fwon8AqHvFLddd1PVWEVZQxkDIz3ngE+hPiluhPZZzPZmZXFD1U7SLfOGJhAa4MsHu2X0RjiZ0QSFm6GlUe0kZ8mpzAHgosiLVE4iPIkUN0L7LGb7F283PHLhfAL89Fw9MokPdh3HanPfxZ5CA4wmaG2Eoi1qJzlLdmU2Br2B1LBUtaMID+JZX+2F6K66U1Cyz+OmpNr8ZGwfSmoa2ZTr/k3WhAeLGwIh8W45NZVdkY0xwiidiUW3SHEjtK2tOVn6LDVT9NiolAj6x4XIwmLhXDqd/QcAS6baSc6SU5kjU1Ki26S4EdpmMdsPCAxNUDtJj+h0Om4c24evD5ZQ3dCidhyhZUYTlO6H2lK1k7RrtbViqbJwUZQUN6J7pLgR2qUo36+38WDXjU7GalP4dJ/0vBFOlO5+RzEUVBfQbGuWnVKi26S4EdpVdhjqSjy+uIkLC2DmwFje31msdhShZSGxkDDCrdbdtO2UkuJGdJcUN0K7LGZ7c7LUyWon6bUbx/bh22PV5JTWqh1FaJnRvY5iyK7MJiE4gXBDuNpRhIeR4kZol8UMfaeCX4DaSXptzuB4IoP8ZGGxcC6jCerLoPSg2kkAe3Eji4lFT0hxI7SppdHelMzDp6Ta+Pv6cM2oZD7cfZwWq03tOEKrUieBb6DbTE3lVOTIlJToESluhDYVfWNvSqaR4gbsPW/K65rYkHNK7ShCq3wNkDYNLOqfM1XRWMGpM6cYGCXFjeg+KW6ENlnMEJIAcdo5bG9YcjgXxYfyyV7ZNSWcyGiCwi3Q3KBqDDl2QfSGFDdCmyyZ9i/SOp3aSRzq6lFJrDtUSn1Tq9pRhFYZTWBtso9+qii7IpsAfQCpoXLsgug+KW6E9tSWQOkBTU1Jtbl6ZBJnWqysO+Q+jdaExsReBKFJqncrzqnMoX9Ef/Q+elVzCM8kxY3QHg8/cuF8UqKCGNc3ko/3Hlc7itCq9qMY1F1UnFOZI52JRY9JcSO0x2KGxJH2pmQadM2oJDYeLed0XZPaUYRW9TdB2SGoOanK7VtsLViqLAyIHKDK/YXnk+JGaIvN9v16G426YkQSOuDL/ep84xFeoN8sQKfa6E1+dT4tthZZTCx6TIoboS1lB+1NyDRc3EQF+zN9QAwfy64p4SzB0ZA0CvLUWXfTfuyCbAMXPSTFjdAWixn8giBlotpJnOra0cnsKqykuELd7bpCw4wm+yiozfVNI3MqckgKTiLMP8zl9xbaIMWN0BaL2d6EzNegdhKnmjs4nkA/PZ9+K6M3wkmMJmgoh9L9Lr91dmW2dCYWvSLFjdCO5gZ78zENT0m1CTb4csnQeD7ecxzFTQ45FBrTZwL4BUOu67sVZ1dky5SU6BUpboR2FH5jbz7mBcUN2HdNHS2r40iJnBQunMDXH/pNd/mi4vIz5ZxuPC2LiUWvSHEjtMNihrBkiPGOn/imD4glMshPet4I5zGaoGgrNNe77Jbti4llWkr0ghQ3QjssZjDO1tyRC+fip/fhihGJfLb3BDabTE0JJzCawNYCBZtddsucihwCfQNJCU1x2T2F9khxI7Sh5gScOuw1U1JtrhmVzInqRnYUVKgdRWhRdH8IT3Hp1FROZQ4DIgbIsQuiV6S4EdpgyQR0kD5b7SQuNTY1ksTwAL6Qhn7CGXQ6+2ioC4ub3Kpc+kf2d9n9hDZJcSO0wWKGpNEQFKV2Epfy8dFx2bBEvjpQglWmpoQzGOdAeTZUH3P6rWyKjYKaAtLD051+L6FtUtwIz2ez2TupetmUVJsrRiRwqraJnTI1JZyh3wzQ+bjklPCT9Sc503qGfuH9nH4voW1S3AjPV7IPGk57bXEzOiWShLAAOWtKOEdQFCSNccnUVF5VHgDGCKPT7yW0TYob4fksGeAfAn3Gq51EFT4+Oi4bnsBXB0pk15RwDqPJPjpqszr1NnnVeQT6BpIYnOjU+wjtk+JGeD5LJqRNtzcd81JXDE+krLaJnYWVakcRWmQ0wZlKOLnXqbfJr84nLSwNH518axK9I3+DhGdrqrM3GfPSKak2Y1Jlako4UZ9x4B/q9KkpS5VF1tsIh5DiRni2ws32JmNeXtz4+Oi4dFgCXx04KVNTwvH0fvaFxU5cVKwoCnnVebLeRjiEFDfCs1nMEJ4K0fIF8YoRiZTWNLGrSKamhBMYZ0PxNmhyzllmpxtPU9NcI9vAhUNIcSM8m5cduXA+Y1MjiQ8z8MU+mZoSTmA0ga0VCjY55eXzq/MBpLgRDiHFjfBcVcVQnuP1U1Jt2hr6rZFdU8IZotIhoi/kZjjl5fOq8vDV+ZISJmdKid6T4kZ4rrxMe3Ox9JlqJ3Eblw9PpKSmkT3FMjUlHEyn+35LuBNYqi2khqXi5+PnlNcX3kWKG+G5LGZIHguBkWoncRvj+kYSF2rgi30lakcRWtR/DpzOhcpCh790XnWeTEkJh5HiRngmmxXysmRK6kfsU1Oya0o4Sdp00OmdMnqTX5Uv28CFw0hxIzzTyb32pmJS3Jzl8uGJnKxuZE9xldpRhNYERth73ji4301tcy1lZ8pIj5CRG+EYUtwIz5RrBkOYfVpKdDAuLYqYEANrD8rUlHACo8k+amptddhL5lV/d6ZUuLR0EI4hxY3wTBazvamYXhYf/pjeR8fFQ+L4+mAJiiJTU8LBjCZorIYTexz2knlVeejQkRae5rDXFN5NihvheRpr4Nh2e38b0alLhiRQcLqBo2V1akcRWpM0BgzhDp2ayq/OJykkiUDfQIe9pvBuUtwIz1Owyd5MTNbbnNNkYzTB/nqZmhKOp/eF9BkOLW7yqvNkMbFwKCluhOexmCEyzd5UTHQqwE/PrIviWHuoVO0oQouMJji2wz495QCWKoustxEOJcWN8DwWMxjnqJ3C7V0yNJ59x6o5UXVG7ShCa4wmUKyQv7HXL9XY2sjxuuOyU0o4lBQ3wrNUFkCFRaakumD2oDj89Dr+e1hGb4SDtY2cOmBqqrCmEAVFGvgJh5LiRngWS6a9iVi/6WoncXthAX5MSo9m7UEpboQTGE0OKW7atoHLmhvhSFLcCM9iMUOf8RAQrnYSj3DJ0AS25p2muqFF7ShCa4wmqMyHirxevUxedR4xgTGEG+TftHAcKW6E57C2Qv56mZLqhosHx9NqU8jMLlM7itCatqMYLL07isFSZZEpKeFwqhc3L730EmlpaQQEBDBx4kS2b99+3uurqqpYvHgxiYmJGAwGBg4cyJdffumitEJVJ/bYd2dIcdNlCeEBjEyJYO0h2RIuHCwgDFIm9HpqKr9azpQSjtfj4iYjI4Mrr7wSo9GI0Wjkyiuv5L///W+3XuPdd99l6dKlPPHEE+zevZuRI0cyb948yso6/ymzubmZiy++mIKCAt5//32ys7NZtWoVycnJPf00hCexmO3TUUmj1U7iUS4ZEk9W9ikaW6xqRxFaYzRB/oYeH8XQamuloKYAY4RsAxeO1aPi5uWXX+bSSy8lNDSUBx54gAceeICwsDAuv/xyXnrppS6/zvPPP88dd9zBwoULGTJkCCtXriQoKIjXX3+90+tff/11Kioq+Pjjj5k6dSppaWnMnDmTkSNH9uTTEJ7GYoZ+M+1NxESXzRsaT0OzlW8s5WpHEVpjnANNNXB8V4+efqz2GK22VpmWEg7Xo+LmmWee4YUXXuCdd97h/vvv5/777+ftt9/mhRde4JlnnunSazQ3N7Nr1y7mzp37fRgfH+bOncuWLVs6fc6nn37K5MmTWbx4MfHx8QwbNoxnnnkGq/XcP5E2NTVRU1PT4UN4oMZqe9Ow/tLfpruMsSGkxwTz9QHZNSUcLGkUBESAJaNHT7dUWwCkuBEO16PipqqqiksvvfSsxy+55BKqq7vWsbK8vByr1Up8fHyHx+Pj4ykp6Xx9QF5eHu+//z5Wq5Uvv/ySxx57jOeee46nn376nPdZvnw54eHh7R8pKSldyifcTP4Ge9OwdDlPqrt0Oh0XD43nv4dLsdrkIE3hQD56SJ/V43U3+dX5hPqFEhMY49hcwuv1qLi5+uqr+eijj856/JNPPuHKK6/sdahzsdlsxMXF8eqrrzJ27FhuvvlmHnnkEVauXHnO5yxbtozq6ur2j+LiYqflE05kMUN0f4jsq3YSj3TJkARO1zezu6hS7ShCa4wm+7TUme7/3cqryiM9Ih2dTueEYMKb9WjxwpAhQ/jDH/5AVlYWkydPBmDr1q1s3ryZX//61/zlL39pv/b+++/v9DViYmLQ6/WUlnYcKi8tLSUhIaHT5yQmJuLn54der29/bPDgwZSUlNDc3Iy/v/9ZzzEYDBgMhm5/jsLNWMww4BK1U3is0SkRxIT489/DpYxPi1I7jtAS42xQbPbR1SHXdOupBTUFMiUlnKJHxc1rr71GZGQkhw4d4tChQ+2PR0RE8Nprr7X/WqfTnbO48ff3Z+zYsWRkZHDttdcC9pGZjIwM7r333k6fM3XqVN5++21sNhs+PvZBp5ycHBITEzstbIRGVOTZj12QLeA95uOjY/ZFcZgPl7HsssFqxxFaEpEK0QPsP4B0s7gprClkdopMNQvH61Fxk5+f75CbL126lAULFjBu3DgmTJjAiy++SH19PQsXLgTgtttuIzk5meXLlwNw991387e//Y0HHniA++67j6NHj/LMM8+cs4ASGmExg48vpE1TO4lHMw2K471dxyiuaCAlKkjtOEJLjCbI/goUBbo4xVTVWEVNcw2pYalODie8kap7am+++WZOnTrF448/TklJCaNGjWLNmjXti4yLioraR2gAUlJS+Prrr/nVr37FiBEjSE5O5oEHHuChhx5S61MQrmDJhJSJYAhVO4lHmzYgBj+9DvORMhZMSVM7jtASowm2/599lDW6az1rimqLAOgbJuvohON1ubhZunQpTz31FMHBwSxduvS81z7//PNdDnDvvfeecxoqKyvrrMcmT57M1q1bu/z6wsNZW+xz+VNldK63QgP8mNgvmgwpboSjpU0DH7/vFv53rbgprCkEIDVURm6E43W5uNmzZw8tLS3t/38usupdONTxXfYmYbLexiFMg+JY8dUR6ptaCTZIM0ThIIYQSJ1kL24m3NGlpxTVFhETGEOQn0yRCsfr8le3zMzMTv9fCKeymCEwEhJHqZ1EE+YMjuPJzw+xKbeceUM735UoRI8YZ8PG5+2jrXq/C15eWFMoozbCaVQ/OFOI87KY7U3CfPQXvFRcWN/oYNJjgzEfllPChYMZTdBcZ+8k3gVFNUWy3kY4TY/Gpevr61mxYgUZGRmUlZVhs9k6/H5eXp5Dwgkvd6bSPi015ja1k2jKnEFxfLL3BDabgo+PTCMLB0kYCYFRkJsBfaec91JFUSiqKWJu37nnvU6InupRcfPLX/6S9evXc+utt5KYmCjrbIRz5K23NweTIxccyjQonlUb8zl4oobhfcLVjiO0wsfHPjVlMcOcx857aWVTJbUttTJyI5ymR8XNV199xRdffMHUqVMdnUeI71nMEDMQIuQ8MEcalxZJaIAvGUdKpbgRjmU0wYF7oaECgs7dCbuoxr4NXNbcCGfp0ZqbyMhIoqKkhbtwIkWx97cxyingjuan92HmwFjMR2TdjXCw9NmAAnlZ572sfRu4NPATTtKj4uapp57i8ccfp6GhwdF5hLA7bYHqItkC7iSmQXHsO1ZNWU2j2lGEloQnQ+ygC54SXlhTSFxQHIG+gS4KJrxNl6elRo8e3WFtTW5uLvHx8aSlpeHn13Hb3+7dux2XUHgni9neFCxNpj6dYdZFceh0kJldxs3j5adn4UBGExz69LxHMRTXFst6G+FUXS5u2g63FMIlLGZ7UzD/YLWTaFJUsD9jUiMxH5HiRjiYcQ5sfRnKj0LswE4vKawpZEj0EBcHE96ky8XNE0884cwcQnyvtRkKNsL0X6udRNNMg+J4KTOXplYrBl/pIyQcpO8U0Pvbf0DppLhRFIWi2iIu7XepCuGEt+jRmpvi4mKOHTvW/uvt27ezZMkSXn31VYcFE17s2A57MzCjbAF3pjmD42hotrI1r0LtKEJL/IMgdTJYMjr97dONp6lvqadvqExLCefpUXHzs5/9rP0IhpKSEubOncv27dt55JFHePLJJx0aUHghSwYERdubggmnuSg+lMTwANZnn1I7itAaowkKNkFr01m/1b4NXHZKCSfqUXFz4MABJkyYAMB//vMfhg8fzjfffMNbb73Fm2++6ch8whtZzPYtpT5yOogz6XQ6Zg6MZX2ObAkXDmY0QUsDFG8767fatoGnhEr/KuE8Pfru0dLSgsFgAOC///0vV199NQCDBg3i5MmTjksnvE/9aTixV7aAu8jMgbFYTtVzrFLaOggHih8GwbGdbgkvqi0iITiBAN8AFYIJb9Gj4mbo0KGsXLmSjRs3sm7dOi691L4w7MSJE0RHRzs0oPAy+VmAIuttXGRK/xj0Pjo25JSrHUVoiY+PffS1k+KmsKZQ1tsIp+tRcfPss8/yf//3f8yaNYv58+czcqR9bcSnn37aPl0lRI9YzBA7GMKS1E7iFcID/RidEiFTU8LxjCY4+S3Udyyci2qKZL2NcLpuny2lKArp6ekUFRXR2tpKZGRk++/deeedBAUFOTSg8CJtRy4MvU7tJF5l5sBY/m9DHi1WG356WeckHKRt9DUvC4b/BPh+G/iV6Veql0t4hW5/JVMUhf79+1NSUtKhsAFIS0sjLi7OYeGElynPgZrjMiXlYjMviqWuqZXdhZVqRxFaEppgX3vzg6mp8jPlnGk9IyM3wum6Xdz4+PgwYMAATp8+7Yw8wptZzKA3QOoUtZN4lWFJ4UQF+7M+R7aECwczfrfuRlGA73dKydELwtl6NAa9YsUKfvOb33DgwAFH5xHeLDcD+k62NwETLuPjo2PGgBgpboTjGU1QexLKDgP2nVI6dPQJ7aNyMKF13V5zA3DbbbfR0NDAyJEj8ff3JzCw48muFRXS8VR0U2uTvenX7GVqJ/FKMy+K5eO9JyirbSQuVLboCgdJnQy+AfbRm/ghFNYUkhiciEFvUDuZ0LgeFTcvvviig2MIr1e0FVrPSH8blUwfEAvAxpxybhgrP1ULB/ELtJ81ZTHDlHtlp5RwmR4VNwsWLHB0DuHtLGYIjoO4oWon8UoxIQaGJYexPueUFDfCsYwmMD8NLY0U1hYyJm6M2omEF+jxvk+LxcKjjz7K/PnzKSuz98j46quvOHjwoMPCCS9iMdsXH8qRC6qZOTCWjUdPYbUpakcRWmI0QWsjtsLNFNcUkxoqIzfC+Xr0nWT9+vUMHz6cbdu28eGHH1JXVwfAt99+yxNPPOHQgMIL1J2Ckn1gnKN2Eq82c2AclQ0tHDherXYUoSVxQyAkgbKjX9FobZSdUsIlelTcPPzwwzz99NOsW7cOf3//9sdNJhNbt251WDjhJfKy7P9Nn6VmCq83OjWCUIOv7JoSjqXTgdFEUdEmAFLC5MBM4Xw9Km7279/Pdded3UU2Li6O8nI5o0Z0k8UM8cMhNF7tJF7NT+/D1P6yJVw4gdFEYV0RPviQEiLFjXC+HhU3ERERnZ7+vWfPHpKTk3sdSngRRfl+vY1Q3cyLYtlTVEl1Q4vaUYSWpM+i2NePRP8w/PR+aqcRXqBHxc1Pf/pTHnroIUpKStDpdNhsNjZv3syDDz7Ibbfd5uiMQsvKDkFdiRQ3bmLGwFhsCmzMldEb4UAhsRSGRtPXKovVhWv0qLh55plnGDRoECkpKdTV1TFkyBBmzJjBlClTePTRRx2dUWiZxWxv8iVHLriF5IhABsSFsEGmpoSDFRkCSa09DTab2lGEF+hRnxt/f39WrVrFY489xoEDB6irq2P06NEMGDDA0fmE1lnM0Hcq+ElXXHcxc2Asn+07gaIo6HQ6teMIDbApNoptZ7jhTC2UHYSE4WpHEhrXo+KmTWpqKikp9sVh8kVQdFvLGSj8BkyPqZ1E/MCMgbH8fVM+2aW1DEoIUzuO0IDS+lKabC2k2nzsP9BIcSOcrMcd01577TWGDRtGQEAAAQEBDBs2jL///e+OzCa0rmgLtDbKkQtuZkK/KAL8fFifLVNTwjEKa787DTx+lL24EcLJelTcPP744zzwwANcddVVvPfee7z33ntcddVV/OpXv+Lxxx93dEahVRYzhCRA3GC1k4gfCPDTMyk9mg1HpbgRjlFUU4RepyfJeDEUboHmBrUjCY3r0bTUK6+8wqpVq5g/f377Y1dffTUjRozgvvvu48knn3RYQKFhlkz7qI1MabqdmQNjWf7lEeqbWgk29Gr2WggKawpJCknCr//FsPZRKPoG+s9VO5bQsB6N3LS0tDBu3LizHh87diytra29DiW8QG0JlB6A/nLkgjuaOTCWZquNrXmn1Y4iNKD9NPDYiyAs2f6DjRBO1KPi5tZbb+WVV1456/FXX32VW265pdehhBeQIxfcWr+YYFKiAqVbsXCIotoi+ob2/e4ohtmy7kY4XZfHm5cuXdr+/zqdjr///e+sXbuWSZMmAbBt2zaKioqkiZ/oGosZEkdCcIzaSUQndDodMwfGSnEjes1qs1JcW8xNF91kf8Bogj3/hpqTEJaobjihWV0ubvbs2dPh12PHjgXAYrEAEBMTQ0xMDAcPHnRgPKFJNpt9WHq0jPK5s5kD4/j31iIKyutJiwlWO47wUCUNJbTYWr4/DbzfLEAHeZkw6mcqJhNa1uXiJjNT5kiFg5QdhPoy2QLu5iYbo/HT61ifc0qKG9FjhTXfbQMP/a64CY6GpFH20VspboST9LjPjRA9ZjGDXxCkTFQ7iTiPEIMv4/pGydSU6JWimiJ8db4khvxgCsposo/eylEMwkl6tMezsbGRv/71r2RmZlJWVobtR39Bd+/e7ZBwQqMsZkibBr4GtZOIC5gxMJa/ZBylqdWKwVevdhzhgQprCukT2gdfnx98uzGaYONzULrfvvZOCAfrUXGzaNEi1q5dy09+8hMmTJggRy+IrmtusDfxuvj3aicRXTBzYCzPrjnCzoJKpvaXxd+i+4pqv9sG/kN9JoBfMORmSHEjnKJHxc3nn3/Ol19+ydSpUx2dR2hd4TdgbZL1Nh5icGIosaEG1ueckuJG9EhRTRHTkqd1fNDXH/pNt4/iTl/a+ROF6IUerblJTk4mNDTU0VmEN7CY7U28YgaqnUR0QfuWcDlnSvRAq62VY3XHzh65AfsPOEVbobne9cGE5vWouHnuued46KGHKCwsdHQeoXUWs72Jl0xleoyZA2PJLq3lZPUZtaMID3Oy/iStttbvd0r9kNEEthYo2Oz6YELzelTcjBs3jsbGRtLT0wkNDSUqKqrDhxCdqjkBpw7LlJSHmT4gBh8dMnojuq2opgig85Gb6P4QngKWDBenEt6gR2tu5s+fz/Hjx3nmmWeIj4+XBcWiayyZgO67Jl7CU0QE+TMmNRLzkTJ+OqGTb1JCnENhTSF+Pn4kBnfSibj9KAbpoSYcr0fFzTfffMOWLVsYOVJWuYtusJjtzbuCo9VOIrpp9qA4XsrMpbHFSoCfbAkXXVNUW0Sf0D7ofc7xd8Zogt3/hOpjEN7HteGEpvVoWmrQoEGcOSPz76IbbDZ7u3WjnALuieYMjqOh2cq2/Aq1owgPUlhT2Pl6mzb9ZoLOR0ZvhMP1qLhZsWIFv/71r8nKyuL06dPU1NR0+BDiLCX7oOG0rLfxUBfFh5IcEUjmkTK1owgPUlTTSY+bHwqKgqQxckq4cLgeTUtdeumlAMyZ0/GncEVR0Ol0WK3W3icT2mIxg38I9BmvdhLRAzqdDtOgODKOlPLEVUNknZ24oBZbC8frjn9/YOa5GE2wYxXYrHCu6SshuqlHxY0coim6zWKGtOn25l3CI5kGx/GvrYXkltUxIF76XInzO1F3AqtiPf/IDdiLmw1/hJN7IXmsS7IJ7etRcTNz5kxH5xBa1lRnb9Y17xm1k4hemJweTaCfnowjZVLciAtq2wZ+3jU3AH3GgX+o/QcgKW6Eg/T4VPCNGzfy85//nClTpnD8+HEA/vWvf7Fp0yaHhRMaUbjZ3qxL1tt4tAA/PVP7R2M+LOtuxIUV1Rbh7+NPfHD8+S/U+0G/GbKoWDhUj4qbDz74gHnz5hEYGMju3btpamoCoLq6mmeekZ/OxY9YzBCeCtFGtZOIXjINimdXUSVVDc1qRxFurrCmkJTQFHx0Xfg2Y5wNxdugqdb5wYRX6FFx8/TTT7Ny5UpWrVqFn59f++NTp05l9+7dDgsnNEKOXNAM06A4rDaF9TnSrVic3wV3Sv2Q0QS2ViiQkX/hGD0qbrKzs5kxY8ZZj4eHh1NVVdXbTEJLqoqhPEempDQiITyAoUlhmGVLuLiAwprCC++UahOVDhF9ZUu4cJgeFTcJCQnk5uae9fimTZtIT0/vdSihIXmZ9iZd6bIIXSvmDIojK/sUrVab2lGEm2qxtnCi/kTXR250Oug/R4ob4TA9Km7uuOMOHnjgAbZt24ZOp+PEiRO89dZbPPjgg9x9992Ozig8WdsOiMBItZMIBzENjqf6TAu7i6rUjiLc1LG6Y9gU24V3Sv2Q0QSnc6Gy0HnBhNfo0Vbwhx9+GJvNxpw5c2hoaGDGjBkYDAYefPBB7rvvPkdnFJ7KZrXvgJj4/9ROIhxoRHI4MSH+mI+UMaFflNpxhBs672ng55I2HXR6+w9E4xY6KZnwFj0audHpdDzyyCNUVFRw4MABtm7dyqlTp3jqqaccnU94shN7obFK1ttojI+PjlkXxWE+Uqp2FOGmimqLCNAHEBcU1/UnBUbYe97I1JRwgG6N3PziF7/o0nWvv/56t0K89NJL/O///i8lJSWMHDmSv/71r0yYMOGCz1u9ejXz58/nmmuu4eOPP+7WPYULWMxgCJPGXBo0Z1Ac7+86RnFFAylRQWrHEW6msKaQlLAubgP/IaMJtr4M1lbQ92hiQQigmyM3b775JpmZmVRVVVFZWXnOj+549913Wbp0KU888QS7d+9m5MiRzJs3j7Ky8+/GKCgo4MEHH2T69Ondup9wIYvZ3pxL73fha4VHmTYgBj+9jv8eltEbcbYLngZ+LkYTNFbDiT2ODyW8SreKm7vvvpvq6mry8/OZPXs2r732Gh999NFZH93x/PPPc8cdd7Bw4UKGDBnCypUrCQoKOu/oj9Vq5ZZbbuH3v//9BXdnNTU1yanlamisgWPb7f1thOaEBvgx2RjDukNS3Iiz5VXn0S+8X/efmDQGDOEyNSV6rVvFzUsvvcTJkyf57W9/y2effUZKSgo33XQTX3/9NYqidPvmzc3N7Nq1i7lz534fyMeHuXPnsmXLlnM+78knnyQuLo5FixZd8B7Lly8nPDy8/SMlJaXbOUUPFGyyN+VKl+JGqy4ZEs+2/ArpViw6qGuuo6yhjPSIHrQF0ftC+gywZDg+mPAq3V5QbDAYmD9/PuvWrePQoUMMHTqUe+65h7S0NOrq6rr1WuXl5VitVuLjO549Eh8fT0lJSafP2bRpE6+99hqrVq3q0j2WLVtGdXV1+0dxcXG3MooespghMk2OXNCwi4fEY7Up0tBPdJBfnQ9AengPe54ZTXBsp316Soge6vHBmWAfZdHpdCiKgtVqdVSmc6qtreXWW29l1apVxMTEdOk5BoOBsLCwDh/CBSxm2SWlcfFhAYxKiWDtQZmaEt+zVFsASAtL69kLGE2gWCF/o+NCCa/T7eKmqamJd955h4svvpiBAweyf/9+/va3v1FUVERISEi3XismJga9Xk9paccvjqWlpSQkJJx1vcVioaCggKuuugpfX198fX355z//yaeffoqvry8Wi6W7n45whsoCqLBIceMFLhkaz/qcUzS2OP+HG+EZ8qrzSApOIsivh7voItPsxzHIuhvRC90qbu655x4SExNZsWIFV155JcXFxbz33ntcfvnl+Ph0fxDI39+fsWPHkpHx/fyqzWYjIyODyZMnn3X9oEGD2L9/P3v37m3/uPrqq5k9ezZ79+6V9TTuwpJpb8bV7+zzx4S2XDIkgTMtVjYdLVc7inAT+VX5PVtv80NGOYpB9E63GgmsXLmS1NRU0tPTWb9+PevXr+/0ug8//LDLr7l06VIWLFjAuHHjmDBhAi+++CL19fUsXGjvUHnbbbeRnJzM8uXLCQgIYNiwYR2eHxERAXDW40JFFjP0GQ8B4WonEU7WPy4EY2wwaw+VMHdI/IWfIDQvrzqPWSmzevciRhPsWAUVefZRHCG6qVvFzW233YZOp3NogJtvvplTp07x+OOPU1JSwqhRo1izZk37IuOioqIejQoJlVhbIX89TFqsdhLhIpcMTeDdHcW0Wm346uXfqjdrsjZxrO5YzxcTt0mbBj6+9lFgKW5ED+iUnuzh9mA1NTWEh4dTXV0ti4udoXg7vHYxLPovpIxXO41wgT1FlVz38je8e+ckJqZHqx1HqCi7IpuffPYT/nnZPxkdN7p3L/b6ZRAUBT99yzHhhFeRH7OEY1nM9umopF5+YRMeY2SfCOJCDayVhn5er9fbwH/IaIL8DWBt6f1rCa8jxY1wLIsZ+s2Uc2G8iI+PjouHxLP2UEmPmnkK7cirziM6IJpwgwPW2xlN0FQDx3f1/rWE15HiRjjOmSp78y3ZAu51LhmaQHHFGY6U1KodRagorzqv9zul2iSNgoAI2TUlekSKG+E4BRvtzbfkPCmvMzk9mlCDrzT083KWKotjpqQAfPSQPkuKG9EjUtwIx7GYIcpob8IlvIq/rw+zBsWx9lDnx6YI7Wu1tVJYU9izAzPPxWiyT0udqXTcawqvIMWNcBw5csGrXTIknoMnajhW2aB2FKGC43XHabG1YIxw4Hlyxtmg2OwLi4XoBiluhGNU5NmPXZDixmvNuigWf70PX8vUlFfKq8oDHLRTqk1EKkQPkKkp0W1S3AjHsJjtTbfSpqmdRKgkNMCP6QNi+HL/SbWjCBVYqi2E+IUQGxjr2Bc2miDXDLITT3SDFDfCMSyZ0GcCBEhjRG92+fBEdhVWcrL6jNpRhIvlV+eTHp7u8C72GE1QXWQfHRaii6S4Eb1nbbHPicuUlNebOyQeP72Or/bLwmJvk1flwG3gP5Q2DXz8ZGpKdIsUN6L3ju+yN9uS4sbrhQf6MX1ArExNeRlFUcivyXfseps2hhBImQi5GY5/baFZUtyI3rOYITDS3nRLeL3Lhyeys7CSkupGtaMIFyltKKW+pd45xQ1Af5O9j5YcxSC6SIob0Xu5GfZmWz56tZMIN3Bx29TUARm98RZO2Sn1Q0YTNNfZD+YVogukuBG901ABJ3bLlJRoFx7ox7T+MbLuxovkVefh7+NPUkiSc26QMBICo2TdjegyKW5E7+RvsDfZSpcjF8T3Lh+eyI7CCspqZGrKG+RV59EvvB96Z43e+vjYG/pJcSO6SIob0TsWM8QMhIgUtZMIN3LJkAR8fXR8dUBGb7xBXnWe86ak2hhNcGKPfbRYiAuQ4kb0nKLY+9vIlJT4kfAgP6b2j+EL2TXlFfKq8ugX4cAzpTqTPhtQIC/LufcRmiDFjei50xZ7cy0pbkQnLh+eyI4CmZrSusrGSiqbKp0/chOeDLGDZGpKdIkUN6LnLGZ7c62+U9VOItzQJUPi0et0rDkoU1Nalldt3yllDHfggZnnYjTZR4vlKAZxAVLciJ6zmCF1kr3JlhA/EhHkb5+a2idTU1qWV52HXqenb1hf59/MOAdqjkH5UeffS3g0KW5Ez7Q225tqyZSUOI/LhyewvaCCslqZmtKqvKo8UkJT8NP7Of9mfaeA3h8s0q1YnJ8UN6Jnjm23N9WS4kacxyVDEuxTU7JrSrPatoG7hH8QpE6WdTfigqS4ET1jMUNQNCSMUDuJcGORwf5MHxDDJ3tPqB1FOElOZQ4DIwe67oZGExRsgtYm191TeBwpbkTPWMz2rZk+8ldInN+1o5PZVVhJcUWD2lGEg50+c5ryM+WuL25aGqB4m+vuKTyOfGcS3Vd/Gk7slSkp0SVzB8cT6Kfn029l9EZrsiuzAbgo6iLX3TR+GATHytSUOC8pbkT35WcBir0duhAXEGzw5eIh8Xyy9ziKbOHVlJyKHAJ9A0kJdWGHch8f+6ixFDfiPKS4Ed1nMUPsYAhz0iF5QnOuHZ1ETmkdR0pq1Y4iHCinMocBkQPw0bn4W4nRBCe/hfpy195XeAwpbkT3yJELogemD4glMsiPj/ceVzuKcKDsymzXrrdp0zZqLEcxiHOQ4kZ0T3kO1ByH/lLciK7z0/twxYhEPtt7AptNpqa0oMXaQl51HhdFunC9TZvQBPvam1zpdyM6J8WN6J7cDNAbIHWK2kmEh7lmVDInqhvZUSCnOmtBXnUerbZW1y4m/iHjbMiToxhE56S4Ed1jMUPfyfZmWkJ0w9jUSJIjAvlEdk1pQk5lDgADIgaoE8BogtqTUHZYnfsLtybFjei61iZ78yxZbyN6wMdHx9Wjkvhy/0maW21qxxG9lF2RTXJIMiH+Kp0tlzoZfANk15TolBQ3ouuKtkLrGSluRI9dMyqJqoYWNuScUjuK6KXsymx11tu08Qu0nzUlxY3ohBQ3oussZgiOg7ihaicRHmpQQhiDEkJlasrDKYpCTmWOeutt2hhNULgZWuRgVtGRFDei6yxm+yI+OXJB9MLVo5JYd6iEuqZWtaOIHjrdeJqKxgp1toH/kNEErY1QtEXdHMLtyHcp0TV1p6Bkn0xJiV67emQSjS021h6Uk8I9VXbFd8cuqDktBRA3BEISZGpKnEWKG9E1bc2y0uXIBdE7fSKDmNgvivd3HVM7iuih7MpsgnyDSA5NVjeITmf/gcuSqW4O4XakuBFdY8mA+OEQGq92EqEBPxnbh28spzlWKSeFe6KcyhwGRg50/bELnTGaoHQ/1JaqnUS4ETf4myncnqJ8v95GCAe4fHgiQf56PtglxzF4ouwKlY5d6Ez6LPt/82T0RnxPihtxYWWHoK5U1tsIhwk2+HLF8ETe310sxzF4mGZrMwXVBervlGoTEgsJI2TdjehAihtxYRazvVlW6mS1kwgNuXFcCsUVZ9iWL8cxeBJLlYVWpdV9Rm7g+3U3NmkOKeykuBEXZjFD36ngF6B2EqEh49Mi6RsdJAuLPUz7sQuRKh270BmjCerLoOyg2kmEm5DiRpxfyxko/EampITD6XQ6fjKmD1/uPyk9bzxIdmU2KaEpBPsFqx3le6mTwDdQpqZEOyluxPkVbbE3yZLiRjjBDWP70Nhq5ct9J9WOIroopyJH/f42P+ZrgLRpUtyIdlLciPOzmCE0EeIGq51EaFBSRCDT+sfw3q5itaOILlAUhezKbAZGudF6mzb950DhFmiW9gJCihtxIblm+6iNTqd2EqFRPxnbhx0FlRSU16sdRVzAqTOnqGqqcq/FxG2MJrA22afRhdeT4kacW22JfYGeTEkJJ5o3NIHQAF9ZWOwB3ObYhc7EDISwZJmaEoAUN+J82lqa95upbg6haQF+eq4amcQHu49hlZ43bi27Mptgv2CSQ1Q+dqEzOp290aglQ+0kwg1IcSPOzWKGxJH2JllCONGNY/twsrqRzbnlakcR55FTYT92Qeeu09RGE5w6AjUn1E4iVCbFjeiczWZvZy5TUsIFRqVEMDA+hNU7itSOIs7jcMVhBkUNUjvGufWbBejkIE0hxY04h9IDUH9KihvhEjqdjp9NSGXtwVLKahvVjiM6Ud1UTUFNAcNjhqsd5dyCoyFplKy7EVLciHOwmMEvCFImqp1EeInrxvTBV6/jvZ2ysNgdHTxt7/7r1sUN2H8gy5OjGLydFDeicxazvSmWr0HtJMJLhAf6cfXIJN7eViQLi93QgfIDhPqFkhqWqnaU8zOaoOE0lOxTO4lQkRQ34mzNDfbOxDIlJVzsZxP7crzqDBtyTqkdRfzI/vL9DI0Zio/Ozb9t9JkAfsEyNeXl3PxvqVBF4TdgbZbiRrjcyD7hDE0K461thWpHET+gKAr7T+13/ykpAF9/6DddihsvJ8WNOJvFbG+GFeOGXUiFpul0Om6Z2BfzkTJOVJ1RO474TmlDKacbTzMsZpjaUbrGaIKirdAsXa+9lRQ34mwWOXJBqOfqUUkE+fuyeoecN+Uu9pfvBzxgMXEb4xywtUDBZrWTCJVIcSM6qjkBpw7LlJRQTYjBl2tHJ7F6exEtVtnx4g72l+8nPiie2CAPaegZbYTwVJma8mJS3IiOLJmADtJnqZ1EeLGfTehLWW0TGYfL1I4isO+U8phRG/jBUQxS3HgrKW5ER5YMSBoNQVFqJxFebEhSGKNTI2RhsRuw2qwcLD/oOett2hhNUJ4N1dI3yRtJcSO+Z7PZR25kSkq4gVsm9mXj0XKKTjeoHcWrFdQU0NDa4FkjNwD9ZoDOR0ZvvJQUN+J7Jd/CmQopboRbuHJEIuGBfvxra4HaUbza/vL96NAxJHqI2lG6JygKksZIceOl3KK4eemll0hLSyMgIICJEyeyffv2c167atUqpk+fTmRkJJGRkcydO/e814tusJjBPwT6jFc7iRAE+OmZPyGV1TuKqW9qVTuO1zpQfoD08HRC/EPUjtJ9RhPkZYHNqnYS4WKqFzfvvvsuS5cu5YknnmD37t2MHDmSefPmUVbW+ULCrKws5s+fT2ZmJlu2bCElJYVLLrmE48ePuzi5BlkyIW26vQmWEG7gtsl9aWi28v4uWTehlv3l+z1vvU0bownOVMLJvWonES6menHz/PPPc8cdd7Bw4UKGDBnCypUrCQoK4vXXX+/0+rfeeot77rmHUaNGMWjQIP7+979js9nIyMhwcXKNaaqzN72SKSnhRpIiArlsWAJvbM7HJudNuVyTtYmcihzPW2/Tps848A+VqSkvpGpx09zczK5du5g7d277Yz4+PsydO5ctW7Z06TUaGhpoaWkhKqrz3T1NTU3U1NR0+BCdKNxsb3plnK12EiE6+MW0fhScbiAzW7aFu9qRiiO0Kq0Mi/XQkRu9n31hca4UN95G1eKmvLwcq9VKfHx8h8fj4+MpKSnp0ms89NBDJCUldSiQfmj58uWEh4e3f6SkpPQ6tyZZzPamV9H91U4iRAdjUiMZlRLB65vz1Y7idQ6UH8DPx4+BER58FItxNhzbDk21aicRLqT6tFRvrFixgtWrV/PRRx8REBDQ6TXLli2jurq6/aO4WFq6d8pitn8RkCMXhBtaNK0fm3NPc6RERl5daX/5fgZHDcZP76d2lJ4zmsDWCgWb1E4iXEjV4iYmJga9Xk9paWmHx0tLS0lISDjvc//0pz+xYsUK1q5dy4gRI855ncFgICwsrMOH+JGqYijPkfU2wm1dOiyBxPAAXt8kozeudKD8gOcuJm4TlQ4RfWXdjZdRtbjx9/dn7NixHRYDty0Onjx58jmf98c//pGnnnqKNWvWMG7cOFdE1ba8THuzq/SZaicRolN+eh9um5zGx3tPUF7XpHYcr1DdVE1hTaHnFzc6HfSfI8WNl1F9Wmrp0qWsWrWKf/zjHxw+fJi7776b+vp6Fi5cCMBtt93GsmXL2q9/9tlneeyxx3j99ddJS0ujpKSEkpIS6urq1PoUPF9uBiSPhcBItZMIcU7zJ6Tgo4O3txWpHcUrHCw/CHjQSeDnYzTB6VyolOM8vIXqxc3NN9/Mn/70Jx5//HFGjRrF3r17WbNmTfsi46KiIk6ePNl+/SuvvEJzczM/+clPSExMbP/405/+pNan4NlsVnuTK5mSEm4uIsifG8b04V9bC2lqlaZszra/fD+h/qGkhqWqHaX30qaDTi+jN15EpyiKVzWPqKmpITw8nOrqall/A3BsF/zdBL/4GlInqZ1GiPPKLatj7vPr+dONI/nJ2D5qx9G0ezPupdnazKuXvKp2FMd47RIIiYeb/6V2EuECqo/cCJVZzGAIs09LCeHm+seFMGdQHCvXW6SpnxPZFBt7yvYwKm6U2lEcx2iC/PVglaM8vIEUN97OYrY3ufLkrZ7Cq9wzuz+5ZXWsPVR64YtFjxytPEpNcw3jEzR0zpzRBI3VcGKP2kmEC0hx480aa+zNraQrsfAgY/tGMik9ipezcvGyWXWX2VGyA38ff0bEnrvNhsdJGgOGcFl34yWkuPFmBZvsza1kMbHwMItn92ffsWo25ZarHUWTdpTsYETsCAx6g9pRHEfvC+kzpLjxElLceDOLGSL72ZtcCeFBpvWPYUSfcF7OtKgdRXNsio1dZbu0NSXVxjgHju2wT08JTZPixptZMmTURngknU7HPbP6syXvNLsKK9WOoylHK49S3VTNuHgNNkg1zgbFCvkb1E4inEyKG29VkQ8VeVLcCI91yZB4+seF8EpWrtpRNGVn6U78fPy0td6mTWQaRBllasoLSHHjrfIy7U2t+k1XO4kQPeLjo+PumUb+e7iMwyflQE1HaVtvE+Db+WHEHs9okuLGC0hx460sZugzHgLC1U4iRI9dPSqJ5IhAXsmStTeOYFNs7Crdpc0pqTZGE1QW2EeuhWZJceONrK2Qt0GmpITH89P7cNfMdD7fd4KC8nq143i83KpcqpqqtLmYuE3aNPDxldEbjZPixhud2A1N1VLcCE24cVwK0SEG/mqWtTe9taNkh3bX27QJCIM+EyBXihstk+LGG1nM9umopNFqJxGi1wL89CyeZeSjPcfILatVO45H21W6i+Exwwn0DVQ7inMZTVCwEawtaicRTiLFjTeymKHfTHtTKyE0YP7EVBLDA3lh3VG1o3gsm2JjZ8lOxiVoeL1NG6MJmmrg+C61kwgnkeLG25ypgmM7of8ctZMI4TAGXz0PzBnAF/tPcuC4NGjrCUuVhcqmSm2vt2mTNAoCI2XdjYZJceNtCjbam1ily3lSQluuH5NMekwwz63NVjuKR9pZuhNfH19Gxo5UO4rz+eghfZYUNxomxY23sZghuj9E9lU7iRAO5av3YeklA8nMPsXOggq143icHSU7vGO9TRujyT4tdUY6XGuRFDfeRFEgV45cENp1+bBEBieG8cevs+XE8G5QFMW+3kbL/W1+LH02KDbIW692EuEEUtx4k4o8qCqU4kZolo+Pjt/MG8j2/Ao2HpUTw7vKq9bbtIlIgZiBMjWlUVLceBOL2d68Km2a2kmEcJrZF8UxJjWCP62V0Zuu2lm6E1+dl6y3+SGjCSyZ9lFtoSlS3HgTSyakTARDqNpJhHAanU7Hb+YNYt+xar4+WKJ2HI+wo2QHw2KGEeQXpHYU1zKaoLoITsvxHVojxY23sLZA/gYwyi4poX2TjdHMHBjL8q+O0NRqVTuOW2uxtbDlxBYmJU1SO4rr9Z0KPn4yNaVBUtx4i2M7oblW1tsIr/HoFYM5VnmGNzYXqB3Fre0p3UNtSy2zUmapHcX1DCGQOkmKGw2S4sZbWMz2plWJo9ROIoRLDIgP5dZJffmbOZey2ka147itzOJM4gLjGBI1RO0o6jDOtvf/am1WO4lwICluvIXFbG9a5aNXO4kQLrNk7gB89Tr+9LU09uuMoihkFmcyK2UWOp1O7TjqMJqguQ6O7VA7iXAgKW68QUOF/SRwmZISXiYiyJ9fXzyQ93YdY/8xOZbhxyxVFo7XHffOKak2CSMhMEqmpjRGihtvkL/B3qxKjlwQXmj+hFQGxoXy+88OytbwH8k6lkWgbyATEieoHUU9Pj72qSkpbjRFihtvYDHbm1VFpKidRAiX89X78PhVQ9hZWMln+06qHcetZBZnMjVpKga9Qe0o6jKa4MQe+yi30AQpbrROUez9bWRKSnixqf1juHhIPCu+PMyZZtkaDlB+ppz9p/Z795RUm/TZgAJ5WWonEQ4ixY3WnbbYm1QZ56idRAhVPXL5YMrrmnk5K1ftKG5hw7EN6HQ6pveZrnYU9YUnQ+xgsGSonUQ4iBQ3Wmcx25tU9Z2idhIhVJUWE8xds4y8kmXhSEmN2nFUl1WcxajYUUQFRKkdxT0YTWDJkqMYNEKKG62zZNibVBlC1E4ihOoWzzaSFhPMQx/sx2rz3m9ija2NbDmxhZkpM9WO4j6MJqg5BuU5aicRDiDFjZa1NkP+RllvI8R3DL56nr1hBPuOVfHG5ny146hm28ltNFobZb3ND/WdAnp/2TWlEVLcaNmx7dBSL8WNED8wtm8kCyan8dzaHIorGtSOo4rM4kz6hvWlX1g/taO4D/8gSJ0sxY1GSHGjZRYzBEVDwgi1kwjhVn4z7yKigv1Z9uF+r+t9Y1NsrD+2nll9vLgr8bkYTVCwCVqb1E4iekmKGy2zmO1bHH3kbRbih4INvvzhumFsyi3n/V3H1I7jUgfLD1J+plympDpjNEFLAxRvUzuJ6CX5rqdV9afhxF6ZkhLiHGZdFMd1o5N5+ovDnKr1np/UM4szCTeEMypulNpR3E/8MAiOlakpDZDiRqvyswDF3lZcCNGpx64cgq+Pjoc/2OcV01OKorCucB0zkmfg6+Ordhz34+NjH+2W4sbjSXGjVRYzxA2BsCS1kwjhtqKC/fnjT0aQcaSMN78pUDuO0+0p20NBTQHX9L9G7Sjuq/8cOPkt1JernUT0ghQ3WqQokGuWKSkhumDO4HgWTk1j+ZdHOHBc2yeHf3D0A/qE9GF8wni1o7iv9Fn2/1oyVY0hekeKGy06lQ21J2RKSogueviyQQyID+H+d/ZQ39SqdhynqG2uZW3BWq4fcD0+OvnSf06hCfa1NzI15dHkb7gWWcygN0CqHLkgRFcYfPX8df5oSmoaeeLTg2rHcYqv8r+ixdYiU1JdYfxu3Y0XrMPSKilutMhihr6T7U2phBBdkh4bwpPXDOP9Xcf4ZO9xteM43AdHP2B6n+nEBcWpHcX9GU1QVwJlh9VOInpIihutaW2yN6GS9TZCdNsNY5K5dlQSj3x0gILyerXjOMzh04c5dPoQNwy4Qe0oniF1MvgGyNSUB5PiRmuKtkLrGSluhOgBnU7H09cNJybEnzv/tZPaxha1IznEB0c/IDYwlmnJ09SO4hn8Au1nTUlx47GkuNEaixmC4yBuqNpJhPBIIQZf/r5gHCerG7nvnT20Wm1qR+qVM61n+DLvS67tf630tukOowkKN0NLo9pJRA9IcaM1lu+2gMuRC0L0WP+4UF6+ZQwbj5bz9Beeve7iv4X/pballuv6X6d2FM9inAOtjVC0Re0kogfkO6CW1JVByT6ZkhLCAaYPiOX3Vw/lzW8K+NfWQrXj9NgHRz9gYsJEUsJS1I7iWeIGQ0gCWDLUTiJ6QIobLcnLsv+3rQmVEKJXfj6pLwunpvE/nx5k49FTasfptoLqAnaV7uL6AderHcXz6HT2HxRzZd2NJ5LiRkssZogfDqHxaicRQjMevWII0wfEcM9buzlaWqt2nG75MPdDwg3hzOk7R+0onslogrKDUFuidhLRTVLcaIWifLfeRroSC+FIeh8df50/muSIQG75+zYsp+rUjtQllY2VvJf9HtcYr8GgN6gdxzO1jYK3jYoLjyHFjVaUHYK6UllvI4QThAb48e9fTiQ80I/5r24lzwMKnFf3vYqCwqLhi9SO4rlCYiFhhGwJ90BS3GiFxWxvOpU6We0kQmhSTIiBt++YRFigH/NXbSXfjZv8FdcUszp7NYuGLSIqIErtOJ7NaLIfomnz7JYA3kaKG62wmKHvVPALUDuJEJoVG2rg7TsmEmLw5aevbnHbAucve/5CVEAUPx/yc7WjeD6jCerL7GtvhMeQ4kYLWs5A4TcyJSWEC8SFBvDOnZMIMfgy/1X3G8HZf2o/awrWcO+oewn0DVQ7judLnQR+QTI15WGkuNGCoi32ZlP9ZUeEEK4QFxrAO3dMItig59qXNrM5t1ztSAAoisLzu56nf0R/rjZerXYcbfA1QNo0KW48jBQ3WpCbAaGJEDtI7SRCeI24sAA+vHsqI/qEc9vr2/nHNwUoiqJqpvXH1rOzdCdLxy5F76NXNYumGE1QuAWaG9ROIrpIihstsGTa//HpdGonEcKrhAf58cbt47l9ShpPfHqQ3320n+ZWdRaettpaeWHXC0xMmCgHZDqa0QTWJvv0v/AIUtx4utoS+0I3WW8jhCp89T48duUQ/njDCN7fdYxb/r6VU7VNLs/xce7H5FXnsXTcUnTyg45jxQyEsGSZmvIgUtx4Okum/b9y5IIQqrppfArv3DGJ/PJ6LnlhPR/uPuayaaojFUd4budzXJl+JUOih7jknl5Fp7M3SJXixmNIcePpLGZIHAnBMWonEcLrjUuLYs2SGUwfEMvS/3zLba9vp7jCues0imuKuWvdXfQN68ujkx516r28mtEEpw5DzQm1k4gukOLGk9lskJcpU1JCuJGYEAN/mT+aN24fj6WsjotfWM+qDXm0Wh2/Fqf8TDl3rruTUP9QXp77MsF+wQ6/h/hOv1mA7vvRcuHWpLjxZKUHoP6UFDdCuKHZg+JYu3QmPx2fyjNfHWbu8+v519ZCGlusDnn92uZa7lp3F83WZlZevFI6ETtbcDQkjZKpKQ/hFsXNSy+9RFpaGgEBAUycOJHt27ef9/r33nuPQYMGERAQwPDhw/nyyy9dlNTNWMzgFwwpE9VOIoToRIjBl/+5eiif3TuNoUnhPPHJAaasMPPif3OoqG/u8es2WZu433w/J+pPsPLilSSHJDswtTgn4xz7aLkcxeD2VC9u3n33XZYuXcoTTzzB7t27GTlyJPPmzaOsrKzT67/55hvmz5/PokWL2LNnD9deey3XXnstBw4ccHFyN2Ax25tL+cqJv0K4s2HJ4bx0yxgyH5zFlSMSWbnewpQVGdz79m7+s6OYk9VnuvQ6ja2NfJX/Fb/8+pfsL9/PS3NeYkDkACenF+2MJmg4DSX71E4iLkCnqNx1auLEiYwfP56//e1vANhsNlJSUrjvvvt4+OGHz7r+5ptvpr6+ns8//7z9sUmTJjFq1ChWrlx5wfvV1NQQHh5OdXU1YWFhjvtEXK25Hp5Ng4ufgkl3qZ1GCNENFfXNvL2tkHWHy9h3rApFgQFxIUwfEMugxFBiQwzEhhqICTEQGezLvvI9fJ73OWsL1lLXUsfI2JHcN/o+JibKqK1LtTbDH/vB9KUw/ddqpxHnoWpx09zcTFBQEO+//z7XXntt++MLFiygqqqKTz755KznpKamsnTpUpYsWdL+2BNPPMHHH3/Mt99+e9b1TU1NNDV933Oiurqa1NRUhj43AH2gBjp46v0A6WkhhCezKfajE2yKwllfkXUKOp0NpTkSa91IbLUjUVpkd6RaXtD/mRm6b2lFA98/PJD/7woIDQ29YC8nXxfl6VR5eTlWq5X4+PgOj8fHx3PkyJFOn1NSUtLp9SUlJZ1ev3z5cn7/+9+f9fjBXx/tYWohhFCLdMhV2zVqB/B2z3Zt5kXV4sYVli1bxtKlS9t/XVVVRd++fSkqKiI8PFzFZALs04QpKSkUFxd79jShhsh74l7k/XAv8n6oLzQ09ILXqFrcxMTEoNfrKS0t7fB4aWkpCQkJnT4nISGhW9cbDAYMhrMX3IaHh8tfTDcSFhYm74ebkffEvcj74V7k/XBvqu6W8vf3Z+zYsWRkZLQ/ZrPZyMjIYPLkyZ0+Z/LkyR2uB1i3bt05rxdCCCGEd1F9Wmrp0qUsWLCAcePGMWHCBF588UXq6+tZuHAhALfddhvJycksX74cgAceeICZM2fy3HPPccUVV7B69Wp27tzJq6++quanIYQQQgg3oXpxc/PNN3Pq1Ckef/xxSkpKGDVqFGvWrGlfNFxUVISPz/cDTFOmTOHtt9/m0Ucf5Xe/+x0DBgzg448/ZtiwYV26n8Fg4Iknnuh0qkq4nrwf7kfeE/ci74d7kffDM6je50YIIYQQwpFU71AshBBCCOFIUtwIIYQQQlOkuBFCCCGEpkhxI4QQQghN8bri5qWXXiItLY2AgAAmTpzI9u3b1Y7kFZYvX8748eMJDQ0lLi6Oa6+9luzs7A7XNDY2snjxYqKjowkJCeGGG244q2GjcI4VK1ag0+k6nNkm74drHT9+nJ///OdER0cTGBjI8OHD2blzZ/vvK4rC448/TmJiIoGBgcydO5ejR+UYGWewWq089thj9OvXj8DAQIxGI0899RQ/3H8j74ebU7zI6tWrFX9/f+X1119XDh48qNxxxx1KRESEUlpaqnY0zZs3b57yxhtvKAcOHFD27t2rXH755UpqaqpSV1fXfs1dd92lpKSkKBkZGcrOnTuVSZMmKVOmTFExtXfYvn27kpaWpowYMUJ54IEH2h+X98N1KioqlL59+yq33367sm3bNiUvL0/5+uuvldzc3PZrVqxYoYSHhysff/yx8u233ypXX3210q9fP+XMmTMqJtemP/zhD0p0dLTy+eefK/n5+cp7772nhISEKH/+85/br5H3w715VXEzYcIEZfHixe2/tlqtSlJSkrJ8+XIVU3mnsrIyBVDWr1+vKIqiVFVVKX5+fsp7773Xfs3hw4cVQNmyZYtaMTWvtrZWGTBggLJu3Tpl5syZ7cWNvB+u9dBDDynTpk075+/bbDYlISFB+d///d/2x6qqqhSDwaC88847rojoVa644grlF7/4RYfHrr/+euWWW25RFEXeD0/gNdNSzc3N7Nq1i7lz57Y/5uPjw9y5c9myZYuKybxTdXU1AFFRUQDs2rWLlpaWDu/PoEGDSE1NlffHiRYvXswVV1zR4c8d5P1wtU8//ZRx48Zx4403EhcXx+jRo1m1alX77+fn51NSUtLh/QgPD2fixInyfjjBlClTyMjIICcnB4Bvv/2WTZs2cdlllwHyfngC1TsUu0p5eTlWq7W983Gb+Ph4jhw5olIq72Sz2ViyZAlTp05t7yxdUlKCv78/ERERHa6Nj4+npKREhZTat3r1anbv3s2OHTvO+j15P1wrLy+PV155haVLl/K73/2OHTt2cP/99+Pv78+CBQva/8w7+/ol74fjPfzww9TU1DBo0CD0ej1Wq5U//OEP3HLLLQDyfngAryluhPtYvHgxBw4cYNOmTWpH8VrFxcU88MADrFu3joCAALXjeD2bzca4ceN45plnABg9ejQHDhxg5cqVLFiwQOV03uc///kPb731Fm+//TZDhw5l7969LFmyhKSkJHk/PITXTEvFxMSg1+vP2u1RWlpKQkKCSqm8z7333svnn39OZmYmffr0aX88ISGB5uZmqqqqOlwv749z7Nq1i7KyMsaMGYOvry++vr6sX7+ev/zlL/j6+hIfHy/vhwslJiYyZMiQDo8NHjyYoqIigPY/c/n65Rq/+c1vePjhh/npT3/K8OHDufXWW/nVr37VfoCzvB/uz2uKG39/f8aOHUtGRkb7YzabjYyMDCZPnqxiMu+gKAr33nsvH330EWazmX79+nX4/bFjx+Ln59fh/cnOzqaoqEjeHyeYM2cO+/fvZ+/eve0f48aN45Zbbmn/f3k/XGfq1KlntUbIycmhb9++APTr14+EhIQO70dNTQ3btm2T98MJGhoaOhzYDKDX67HZbIC8Hx5B7RXNrrR69WrFYDAob775pnLo0CHlzjvvVCIiIpSSkhK1o2ne3XffrYSHhytZWVnKyZMn2z8aGhrar7nrrruU1NRUxWw2Kzt37lQmT56sTJ48WcXU3uWHu6UURd4PV9q+fbvi6+ur/OEPf1COHj2qvPXWW0pQUJDy73//u/2aFStWKBEREconn3yi7Nu3T7nmmmtk67GTLFiwQElOTm7fCv7hhx8qMTExym9/+9v2a+T9cG9eVdwoiqL89a9/VVJTUxV/f39lwoQJytatW9WO5BWATj/eeOON9mvOnDmj3HPPPUpkZKQSFBSkXHfddcrJkyfVC+1lflzcyPvhWp999pkybNgwxWAwKIMGDVJeffXVDr9vs9mUxx57TImPj1cMBoMyZ84cJTs7W6W02lZTU6M88MADSmpqqhIQEKCkp6crjzzyiNLU1NR+jbwf7k2nKD9ouSiEEEII4eG8Zs2NEEIIIbyDFDdCCCGE0BQpboQQQgihKVLcCCGEEEJTpLgRQgghhKZIcSOEEEIITZHiRgghhBCaIsWNEEIIITRFihshhNf4n//5H0aNGqV2DCGEk0lxI4RwW7NmzWLJkiVqxxBCeBgpboQQQgihKVLcCCHc0u2338769ev585//jE6nQ6fTYbFYWLRoEf369SMwMJCLLrqIP//5zx2el5WVxYQJEwgODiYiIoKpU6dSWFjY6T0sFgvp6ence++9yDF7QmiHr9oBhBCiM3/+85/Jyclh2LBhPPnkkwBERkbSp08f3nvvPaKjo/nmm2+48847SUxM5KabbqK1tZVrr72WO+64g3feeYfm5ma2b9+OTqc76/X37dvHvHnzWLRoEU8//bSrPz0hhBNJcSOEcEvh4eH4+/sTFBREQkJC++O///3v2/+/X79+bNmyhf/85z/cdNNN1NTUUF1dzZVXXonRaARg8ODBZ732N998w5VXXskjjzzCr3/9a+d/MkIIl5JpKSGER3nppZcYO3YssbGxhISE8Oqrr1JUVARAVFQUt99+O/PmzeOqq67iz3/+MydPnuzw/KKiIi6++GIef/xxKWyE0CgpboQQHmP16tU8+OCDLFq0iLVr17J3714WLlxIc3Nz+zVvvPEGW7ZsYcqUKbz77rsMHDiQrVu3tv9+bGwsEyZM4J133qGmpkaNT0MI4WRS3Agh3Ja/vz9Wq7X915s3b2bKlCncc889jB49mv79+2OxWM563ujRo1m2bBnffPMNw4YN4+23327/vcDAQD7//HMCAgKYN28etbW1LvlchBCuI8WNEMJtpaWlsW3bNgoKCigvL2fAgAHs3LmTr7/+mpycHB577DF27NjRfn1+fj7Lli1jy5YtFBYWsnbtWo4ePXrWupvg4GC++OILfH19ueyyy6irq3P1pyaEcCIpboQQbuvBBx9Er9czZMgQYmNjmTdvHtdffz0333wzEydO5PTp09xzzz3t1wcFBXHkyBFuuOEGBg4cyJ133snixYv5f//v/5312iEhIXz11VcoisIVV1xBfX29Kz81IYQT6RRp7iCEEEIIDZGRGyGEEEJoihQ3QgghhNAUKW6EEEIIoSlS3AghhBBCU6S4EUIIIYSmSHEjhBBCCE2R4kYIIYQQmiLFjRBCCCE0RYobIYQQQmiKFDdCCCGE0BQpboQQQgihKf8fV9gp/qatSCEAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import skfuzzy as fuzz\n",
    "\n",
    "task[\"less\"] = fuzz.zmf(task.universe, 20, 60)\n",
    "task[\"ok\"] = fuzz.trapmf(task.universe, [10, 30, 60, 80])\n",
    "task[\"more\"] = fuzz.smf(task.universe, 60, 80)\n",
    "task.view()\n",
    "\n",
    "target[\"less\"] = fuzz.zmf(target.universe, 20, 60)\n",
    "target[\"ok\"] = fuzz.trapmf(target.universe, [10, 30, 60, 80])\n",
    "target[\"more\"] = fuzz.smf(target.universe, 60, 80)\n",
    "target.view()\n",
    "\n",
    "need[\"less\"] = fuzz.zmf(need.universe, 0, 80)\n",
    "need[\"ok\"] = fuzz.trapmf(need.universe, [0, 60, 70, 90])\n",
    "need[\"more\"] = fuzz.smf(need.universe, 80, 110)\n",
    "need.view()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Формирование и визуализация базы нечетких правил\n",
    "\n",
    "В случае ошибки необходимо в файле\n",
    "```\n",
    ".venv/lib/python3.13/site-packages/skfuzzy/control/visualization.py\n",
    "```\n",
    "удалить лишний отступ на 182 строке, должно быть:\n",
    "```python\n",
    "        if not matplotlib_present:\n",
    "            raise ImportError(\"`ControlSystemVisualizer` can only be used \"\n",
    "                              \"with `matplotlib` present in the system.\")\n",
    "\n",
    "        self.ctrl = control_system\n",
    "\n",
    "        self.fig, self.ax = plt.subplots()\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Figure size 640x480 with 1 Axes>, <Axes: >)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rule1 = ctrl.Rule(task[\"less\"] & target[\"more\"], need[\"more\"])\n",
    "rule2 = ctrl.Rule(task[\"less\"] & target[\"ok\"], need[\"more\"])\n",
    "rule3 = ctrl.Rule(task[\"less\"] & target[\"less\"], need[\"less\"])\n",
    "rule4 = ctrl.Rule(task[\"ok\"] & target[\"more\"], need[\"more\"])\n",
    "rule5 = ctrl.Rule(task[\"ok\"] & target[\"ok\"], need[\"ok\"])\n",
    "rule6 = ctrl.Rule(task[\"ok\"] & target[\"less\"], need[\"less\"])\n",
    "rule7 = ctrl.Rule(task[\"more\"] & target[\"more\"], need[\"more\"])\n",
    "rule8 = ctrl.Rule(task[\"more\"] & target[\"ok\"], need[\"less\"])\n",
    "rule9 = ctrl.Rule(task[\"more\"] & target[\"less\"], need[\"less\"])\n",
    "\n",
    "rule1.view()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Создание нечеткой системы и добавление нечетких правил в базу знаний нечеткой системы"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "need_ctrl = ctrl.ControlSystem(\n",
    "    [\n",
    "        rule1,\n",
    "        rule2,\n",
    "        rule3,\n",
    "        rule4,\n",
    "        rule5,\n",
    "        rule6,\n",
    "        rule7,\n",
    "        rule8,\n",
    "        rule9,\n",
    "    ]\n",
    ")\n",
    "\n",
    "needs = ctrl.ControlSystemSimulation(need_ctrl)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Пример расчета выходной переменной influx на основе входных переменных level и flow\n",
    "\n",
    "Система также формирует подробный журнал выполнения процесса нечеткого логического вывода"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=============\n",
      " Antecedents \n",
      "=============\n",
      "Antecedent: task                    = 60\n",
      "  - less                            : 0.0\n",
      "  - ok                              : 1.0\n",
      "  - more                            : 0.0\n",
      "Antecedent: target                  = 20\n",
      "  - less                            : 1.0\n",
      "  - ok                              : 0.5\n",
      "  - more                            : 0.0\n",
      "\n",
      "=======\n",
      " Rules \n",
      "=======\n",
      "RULE #0:\n",
      "  IF task[less] AND target[more] THEN need[more]\n",
      "\tAND aggregation function : fmin\n",
      "\tOR aggregation function  : fmax\n",
      "\n",
      "  Aggregation (IF-clause):\n",
      "  - task[less]                                             : 0.0\n",
      "  - target[more]                                           : 0.0\n",
      "                               task[less] AND target[more] = 0.0\n",
      "  Activation (THEN-clause):\n",
      "                                                need[more] : 0.0\n",
      "\n",
      "RULE #1:\n",
      "  IF task[less] AND target[ok] THEN need[more]\n",
      "\tAND aggregation function : fmin\n",
      "\tOR aggregation function  : fmax\n",
      "\n",
      "  Aggregation (IF-clause):\n",
      "  - task[less]                                             : 0.0\n",
      "  - target[ok]                                             : 0.5\n",
      "                                 task[less] AND target[ok] = 0.0\n",
      "  Activation (THEN-clause):\n",
      "                                                need[more] : 0.0\n",
      "\n",
      "RULE #2:\n",
      "  IF task[less] AND target[less] THEN need[less]\n",
      "\tAND aggregation function : fmin\n",
      "\tOR aggregation function  : fmax\n",
      "\n",
      "  Aggregation (IF-clause):\n",
      "  - task[less]                                             : 0.0\n",
      "  - target[less]                                           : 1.0\n",
      "                               task[less] AND target[less] = 0.0\n",
      "  Activation (THEN-clause):\n",
      "                                                need[less] : 0.0\n",
      "\n",
      "RULE #3:\n",
      "  IF task[ok] AND target[more] THEN need[more]\n",
      "\tAND aggregation function : fmin\n",
      "\tOR aggregation function  : fmax\n",
      "\n",
      "  Aggregation (IF-clause):\n",
      "  - task[ok]                                               : 1.0\n",
      "  - target[more]                                           : 0.0\n",
      "                                 task[ok] AND target[more] = 0.0\n",
      "  Activation (THEN-clause):\n",
      "                                                need[more] : 0.0\n",
      "\n",
      "RULE #4:\n",
      "  IF task[ok] AND target[ok] THEN need[ok]\n",
      "\tAND aggregation function : fmin\n",
      "\tOR aggregation function  : fmax\n",
      "\n",
      "  Aggregation (IF-clause):\n",
      "  - task[ok]                                               : 1.0\n",
      "  - target[ok]                                             : 0.5\n",
      "                                   task[ok] AND target[ok] = 0.5\n",
      "  Activation (THEN-clause):\n",
      "                                                  need[ok] : 0.5\n",
      "\n",
      "RULE #5:\n",
      "  IF task[ok] AND target[less] THEN need[less]\n",
      "\tAND aggregation function : fmin\n",
      "\tOR aggregation function  : fmax\n",
      "\n",
      "  Aggregation (IF-clause):\n",
      "  - task[ok]                                               : 1.0\n",
      "  - target[less]                                           : 1.0\n",
      "                                 task[ok] AND target[less] = 1.0\n",
      "  Activation (THEN-clause):\n",
      "                                                need[less] : 1.0\n",
      "\n",
      "RULE #6:\n",
      "  IF task[more] AND target[more] THEN need[more]\n",
      "\tAND aggregation function : fmin\n",
      "\tOR aggregation function  : fmax\n",
      "\n",
      "  Aggregation (IF-clause):\n",
      "  - task[more]                                             : 0.0\n",
      "  - target[more]                                           : 0.0\n",
      "                               task[more] AND target[more] = 0.0\n",
      "  Activation (THEN-clause):\n",
      "                                                need[more] : 0.0\n",
      "\n",
      "RULE #7:\n",
      "  IF task[more] AND target[ok] THEN need[less]\n",
      "\tAND aggregation function : fmin\n",
      "\tOR aggregation function  : fmax\n",
      "\n",
      "  Aggregation (IF-clause):\n",
      "  - task[more]                                             : 0.0\n",
      "  - target[ok]                                             : 0.5\n",
      "                                 task[more] AND target[ok] = 0.0\n",
      "  Activation (THEN-clause):\n",
      "                                                need[less] : 0.0\n",
      "\n",
      "RULE #8:\n",
      "  IF task[more] AND target[less] THEN need[less]\n",
      "\tAND aggregation function : fmin\n",
      "\tOR aggregation function  : fmax\n",
      "\n",
      "  Aggregation (IF-clause):\n",
      "  - task[more]                                             : 0.0\n",
      "  - target[less]                                           : 1.0\n",
      "                               task[more] AND target[less] = 0.0\n",
      "  Activation (THEN-clause):\n",
      "                                                need[less] : 0.0\n",
      "\n",
      "\n",
      "==============================\n",
      " Intermediaries and Conquests \n",
      "==============================\n",
      "Consequent: need                     = 35.97074517705886\n",
      "  less:\n",
      "    Accumulate using accumulation_max : 1.0\n",
      "  ok:\n",
      "    Accumulate using accumulation_max : 0.5\n",
      "  more:\n",
      "    Accumulate using accumulation_max : 0.0\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "np.float64(35.97074517705886)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "needs.input[\"task\"] = 60\n",
    "needs.input[\"target\"] = 20\n",
    "needs.compute()\n",
    "needs.print_state()\n",
    "needs.output[\"need\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Визуализация функции принадлежности для выходной переменной influx\n",
    "\n",
    "Функция получена в процессе аккумуляции и используется для дефаззификации значения выходной переменной influx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "need.view(sim=needs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Пример решения задачи регрессии на основе нечеткого логического вывода"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Загрузка данных"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'houses_train'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>sqft_living</th>\n",
       "      <th>sqft_lot</th>\n",
       "      <th>floors</th>\n",
       "      <th>waterfront</th>\n",
       "      <th>view</th>\n",
       "      <th>condition</th>\n",
       "      <th>grade</th>\n",
       "      <th>sqft_above</th>\n",
       "      <th>sqft_basement</th>\n",
       "      <th>yr_built</th>\n",
       "      <th>yr_renovated</th>\n",
       "      <th>sqft_living15</th>\n",
       "      <th>sqft_lot15</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9848</th>\n",
       "      <td>450000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1610</td>\n",
       "      <td>6160</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>1610</td>\n",
       "      <td>0</td>\n",
       "      <td>1977</td>\n",
       "      <td>0</td>\n",
       "      <td>1750</td>\n",
       "      <td>6305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3710</th>\n",
       "      <td>361000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1.50</td>\n",
       "      <td>1200</td>\n",
       "      <td>7236</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>1200</td>\n",
       "      <td>0</td>\n",
       "      <td>1975</td>\n",
       "      <td>0</td>\n",
       "      <td>1680</td>\n",
       "      <td>7800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16998</th>\n",
       "      <td>413000.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1410</td>\n",
       "      <td>6000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>810</td>\n",
       "      <td>600</td>\n",
       "      <td>1925</td>\n",
       "      <td>0</td>\n",
       "      <td>1500</td>\n",
       "      <td>4800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1968</th>\n",
       "      <td>490000.0</td>\n",
       "      <td>4</td>\n",
       "      <td>2.25</td>\n",
       "      <td>2020</td>\n",
       "      <td>85813</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>2020</td>\n",
       "      <td>0</td>\n",
       "      <td>1995</td>\n",
       "      <td>0</td>\n",
       "      <td>2120</td>\n",
       "      <td>85813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16407</th>\n",
       "      <td>333000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1050</td>\n",
       "      <td>7560</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>1050</td>\n",
       "      <td>0</td>\n",
       "      <td>1951</td>\n",
       "      <td>0</td>\n",
       "      <td>1490</td>\n",
       "      <td>7560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12454</th>\n",
       "      <td>229000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1.00</td>\n",
       "      <td>810</td>\n",
       "      <td>5100</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>810</td>\n",
       "      <td>0</td>\n",
       "      <td>1955</td>\n",
       "      <td>0</td>\n",
       "      <td>1500</td>\n",
       "      <td>5100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10242</th>\n",
       "      <td>279000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1520</td>\n",
       "      <td>8055</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>1520</td>\n",
       "      <td>0</td>\n",
       "      <td>1952</td>\n",
       "      <td>0</td>\n",
       "      <td>1560</td>\n",
       "      <td>8160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17115</th>\n",
       "      <td>182500.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1.00</td>\n",
       "      <td>910</td>\n",
       "      <td>7194</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>910</td>\n",
       "      <td>0</td>\n",
       "      <td>1971</td>\n",
       "      <td>0</td>\n",
       "      <td>1530</td>\n",
       "      <td>7200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19205</th>\n",
       "      <td>695000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>3.25</td>\n",
       "      <td>3080</td>\n",
       "      <td>12100</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>2080</td>\n",
       "      <td>1000</td>\n",
       "      <td>1984</td>\n",
       "      <td>0</td>\n",
       "      <td>2100</td>\n",
       "      <td>6581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12428</th>\n",
       "      <td>430000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1210</td>\n",
       "      <td>5200</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>1210</td>\n",
       "      <td>0</td>\n",
       "      <td>1941</td>\n",
       "      <td>0</td>\n",
       "      <td>890</td>\n",
       "      <td>5200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>17290 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          price  bedrooms  bathrooms  sqft_living  sqft_lot  floors  \\\n",
       "9848   450000.0         2       2.00         1610      6160     2.0   \n",
       "3710   361000.0         3       1.50         1200      7236     1.0   \n",
       "16998  413000.0         4       1.00         1410      6000     1.0   \n",
       "1968   490000.0         4       2.25         2020     85813     2.0   \n",
       "16407  333000.0         3       1.00         1050      7560     1.0   \n",
       "...         ...       ...        ...          ...       ...     ...   \n",
       "12454  229000.0         2       1.00          810      5100     1.0   \n",
       "10242  279000.0         3       1.00         1520      8055     1.5   \n",
       "17115  182500.0         3       1.00          910      7194     1.0   \n",
       "19205  695000.0         3       3.25         3080     12100     2.0   \n",
       "12428  430000.0         3       1.00         1210      5200     1.0   \n",
       "\n",
       "       waterfront  view  condition  grade  sqft_above  sqft_basement  \\\n",
       "9848            0     0          4      8        1610              0   \n",
       "3710            0     0          3      7        1200              0   \n",
       "16998           0     0          3      7         810            600   \n",
       "1968            0     0          3      7        2020              0   \n",
       "16407           0     0          3      7        1050              0   \n",
       "...           ...   ...        ...    ...         ...            ...   \n",
       "12454           0     0          3      6         810              0   \n",
       "10242           0     0          3      7        1520              0   \n",
       "17115           0     0          4      7         910              0   \n",
       "19205           0     0          3      8        2080           1000   \n",
       "12428           0     0          3      6        1210              0   \n",
       "\n",
       "       yr_built  yr_renovated  sqft_living15  sqft_lot15  \n",
       "9848       1977             0           1750        6305  \n",
       "3710       1975             0           1680        7800  \n",
       "16998      1925             0           1500        4800  \n",
       "1968       1995             0           2120       85813  \n",
       "16407      1951             0           1490        7560  \n",
       "...         ...           ...            ...         ...  \n",
       "12454      1955             0           1500        5100  \n",
       "10242      1952             0           1560        8160  \n",
       "17115      1971             0           1530        7200  \n",
       "19205      1984             0           2100        6581  \n",
       "12428      1941             0            890        5200  \n",
       "\n",
       "[17290 rows x 16 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'houses_test'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>sqft_living</th>\n",
       "      <th>sqft_lot</th>\n",
       "      <th>floors</th>\n",
       "      <th>waterfront</th>\n",
       "      <th>view</th>\n",
       "      <th>condition</th>\n",
       "      <th>grade</th>\n",
       "      <th>sqft_above</th>\n",
       "      <th>sqft_basement</th>\n",
       "      <th>yr_built</th>\n",
       "      <th>yr_renovated</th>\n",
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       "      <th>sqft_lot15</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8943</th>\n",
       "      <td>852600.0</td>\n",
       "      <td>4</td>\n",
       "      <td>2.50</td>\n",
       "      <td>3320</td>\n",
       "      <td>11901</td>\n",
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       "      <td>9</td>\n",
       "      <td>2650</td>\n",
       "      <td>670</td>\n",
       "      <td>1977</td>\n",
       "      <td>0</td>\n",
       "      <td>2700</td>\n",
       "      <td>11114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16711</th>\n",
       "      <td>795000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.75</td>\n",
       "      <td>1820</td>\n",
       "      <td>7517</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
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       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>1820</td>\n",
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       "      <td>1997</td>\n",
       "      <td>0</td>\n",
       "      <td>2540</td>\n",
       "      <td>8035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4156</th>\n",
       "      <td>222000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1.75</td>\n",
       "      <td>1240</td>\n",
       "      <td>7560</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>1070</td>\n",
       "      <td>170</td>\n",
       "      <td>1967</td>\n",
       "      <td>0</td>\n",
       "      <td>1650</td>\n",
       "      <td>7560</td>\n",
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       "    <tr>\n",
       "      <th>20873</th>\n",
       "      <td>375000.0</td>\n",
       "      <td>6</td>\n",
       "      <td>2.25</td>\n",
       "      <td>3206</td>\n",
       "      <td>5793</td>\n",
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       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>3206</td>\n",
       "      <td>0</td>\n",
       "      <td>2012</td>\n",
       "      <td>0</td>\n",
       "      <td>2527</td>\n",
       "      <td>5804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6498</th>\n",
       "      <td>274950.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.25</td>\n",
       "      <td>1570</td>\n",
       "      <td>8767</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>1180</td>\n",
       "      <td>390</td>\n",
       "      <td>1990</td>\n",
       "      <td>0</td>\n",
       "      <td>1570</td>\n",
       "      <td>7434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21109</th>\n",
       "      <td>315000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2.25</td>\n",
       "      <td>1240</td>\n",
       "      <td>705</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>1150</td>\n",
       "      <td>90</td>\n",
       "      <td>2009</td>\n",
       "      <td>0</td>\n",
       "      <td>1240</td>\n",
       "      <td>750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13160</th>\n",
       "      <td>240000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1320</td>\n",
       "      <td>24319</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>7</td>\n",
       "      <td>1320</td>\n",
       "      <td>0</td>\n",
       "      <td>1966</td>\n",
       "      <td>0</td>\n",
       "      <td>1430</td>\n",
       "      <td>98445</td>\n",
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       "    <tr>\n",
       "      <th>3892</th>\n",
       "      <td>695500.0</td>\n",
       "      <td>5</td>\n",
       "      <td>2.75</td>\n",
       "      <td>2510</td>\n",
       "      <td>9180</td>\n",
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       "      <td>1600</td>\n",
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       "      <td>1975</td>\n",
       "      <td>0</td>\n",
       "      <td>2650</td>\n",
       "      <td>9780</td>\n",
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       "    <tr>\n",
       "      <th>15547</th>\n",
       "      <td>910000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2700</td>\n",
       "      <td>6120</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>1350</td>\n",
       "      <td>1350</td>\n",
       "      <td>1962</td>\n",
       "      <td>0</td>\n",
       "      <td>1700</td>\n",
       "      <td>4590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11770</th>\n",
       "      <td>1850000.0</td>\n",
       "      <td>5</td>\n",
       "      <td>3.25</td>\n",
       "      <td>3680</td>\n",
       "      <td>6060</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>2630</td>\n",
       "      <td>1050</td>\n",
       "      <td>1925</td>\n",
       "      <td>0</td>\n",
       "      <td>3050</td>\n",
       "      <td>5850</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4323 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           price  bedrooms  bathrooms  sqft_living  sqft_lot  floors  \\\n",
       "8943    852600.0         4       2.50         3320     11901     2.0   \n",
       "16711   795000.0         3       2.75         1820      7517     1.0   \n",
       "4156    222000.0         3       1.75         1240      7560     1.0   \n",
       "20873   375000.0         6       2.25         3206      5793     2.0   \n",
       "6498    274950.0         3       2.25         1570      8767     1.0   \n",
       "...          ...       ...        ...          ...       ...     ...   \n",
       "21109   315000.0         2       2.25         1240       705     2.0   \n",
       "13160   240000.0         2       1.00         1320     24319     1.0   \n",
       "3892    695500.0         5       2.75         2510      9180     1.0   \n",
       "15547   910000.0         3       2.00         2700      6120     1.0   \n",
       "11770  1850000.0         5       3.25         3680      6060     2.0   \n",
       "\n",
       "       waterfront  view  condition  grade  sqft_above  sqft_basement  \\\n",
       "8943            0     0          5      9        2650            670   \n",
       "16711           0     0          3      9        1820              0   \n",
       "4156            0     0          3      8        1070            170   \n",
       "20873           0     0          3      7        3206              0   \n",
       "6498            0     0          3      7        1180            390   \n",
       "...           ...   ...        ...    ...         ...            ...   \n",
       "21109           0     0          3      7        1150             90   \n",
       "13160           0     0          3      7        1320              0   \n",
       "3892            0     1          4      8        1600            910   \n",
       "15547           0     0          4      8        1350           1350   \n",
       "11770           0     0          5      9        2630           1050   \n",
       "\n",
       "       yr_built  yr_renovated  sqft_living15  sqft_lot15  \n",
       "8943       1977             0           2700       11114  \n",
       "16711      1997             0           2540        8035  \n",
       "4156       1967             0           1650        7560  \n",
       "20873      2012             0           2527        5804  \n",
       "6498       1990             0           1570        7434  \n",
       "...         ...           ...            ...         ...  \n",
       "21109      2009             0           1240         750  \n",
       "13160      1966             0           1430       98445  \n",
       "3892       1975             0           2650        9780  \n",
       "15547      1962             0           1700        4590  \n",
       "11770      1925             0           3050        5850  \n",
       "\n",
       "[4323 rows x 16 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from utils import split_stratified_into_train_val_test\n",
    "import numpy as np\n",
    "import skfuzzy as fuzz\n",
    "import skfuzzy.control as ctrl\n",
    "\n",
    "# Загрузка и обработка данных\n",
    "df = pd.read_csv(\"data/kc_house_data.csv\")\n",
    "df = df.drop([\"id\", \"date\", \"lat\", \"long\"], axis=1)\n",
    "df = df.dropna()\n",
    "\n",
    "# Разбиение на обучающую, валидационную и тестовую выборки\n",
    "houses_train, X_val, houses_test, y_train, y_val, y_test = split_stratified_into_train_val_test(\n",
    "    df,\n",
    "    stratify_colname=\"zipcode\",  # Стратификация по району\n",
    "    target_colname=\"price\",\n",
    "    frac_train=0.80,\n",
    "    frac_val=0,\n",
    "    frac_test=0.20\n",
    ")\n",
    "\n",
    "# Удаляем столбец стратификации\n",
    "houses_train = houses_train.drop(\"zipcode\", axis=1)\n",
    "houses_test = houses_test.drop(\"zipcode\", axis=1)\n",
    "\n",
    "display(\"houses_train\", houses_train)\n",
    "display(\"houses_test\", houses_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Инициализация лингвистических переменных и автоматическое формирование нечетких переменных"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7700000.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\TEMP_UNIVERSITY\\mai\\.venv\\Lib\\site-packages\\skfuzzy\\control\\fuzzyvariable.py:125: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n",
      "  fig.show()\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Определение нечетких переменных\n",
    "yr_built = ctrl.Antecedent(np.arange(1900, 2025, 1), \"yr_built\")\n",
    "sqft_living = ctrl.Antecedent(houses_train[\"sqft_living\"].sort_values().unique(), \"sqft_living\")\n",
    "\n",
    "print(houses_train[\"price\"].max())\n",
    "price = ctrl.Consequent(np.arange(houses_train[\"price\"].min() // 1000, \n",
    "                                  houses_train[\"price\"].max() // 1000, 1), \"price\")\n",
    "\n",
    "# Размытие переменных\n",
    "yr_built[\"old\"] = fuzz.zmf(yr_built.universe, 1900, 2000)\n",
    "yr_built[\"new\"] = fuzz.smf(yr_built.universe, 1990, 2025)\n",
    "yr_built.view()\n",
    "\n",
    "sqft_living.automf(3, variable_type=\"quant\")\n",
    "sqft_living.view()\n",
    "\n",
    "price[\"very cheap\"] = fuzz.trimf(price.universe, [0, 200, 400])\n",
    "price[\"cheap\"] = fuzz.trimf(price.universe, [300, 500, 800])\n",
    "price[\"average\"] = fuzz.trapmf(price.universe, [600, 900, 1500, 2500])\n",
    "price[\"expensive\"] = fuzz.trimf(price.universe, [2000, 3500, 5000])\n",
    "price[\"very expensive\"] = fuzz.smf(price.universe, 4000, 10000)\n",
    "price.view()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Нечеткие правила"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Создание правил\n",
    "rule11 = ctrl.Rule(\n",
    "    sqft_living[\"low\"] & yr_built[\"old\"],\n",
    "    price[\"very cheap\"],\n",
    ")\n",
    "rule12 = ctrl.Rule(\n",
    "    sqft_living[\"average\"] & yr_built[\"old\"],\n",
    "    price[\"cheap\"],\n",
    ")\n",
    "rule13 = ctrl.Rule(\n",
    "    sqft_living[\"high\"] & yr_built[\"old\"],\n",
    "    price[\"average\"],\n",
    ")\n",
    "rule21 = ctrl.Rule(\n",
    "    sqft_living[\"low\"] & yr_built[\"new\"],\n",
    "    price[\"average\"],\n",
    ")\n",
    "rule22 = ctrl.Rule(\n",
    "    sqft_living[\"average\"] & yr_built[\"new\"],\n",
    "    price[\"expensive\"],\n",
    ")\n",
    "rule23 = ctrl.Rule(\n",
    "    sqft_living[\"high\"] & yr_built[\"new\"],\n",
    "    price[\"very expensive\"],\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Создание нечеткой системы"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[IF sqft_living[low] AND yr_built[old] THEN price[very cheap]\n",
       " \tAND aggregation function : fmin\n",
       " \tOR aggregation function  : fmax,\n",
       " IF sqft_living[average] AND yr_built[old] THEN price[cheap]\n",
       " \tAND aggregation function : fmin\n",
       " \tOR aggregation function  : fmax,\n",
       " IF sqft_living[high] AND yr_built[old] THEN price[average]\n",
       " \tAND aggregation function : fmin\n",
       " \tOR aggregation function  : fmax,\n",
       " IF sqft_living[low] AND yr_built[new] THEN price[average]\n",
       " \tAND aggregation function : fmin\n",
       " \tOR aggregation function  : fmax,\n",
       " IF sqft_living[average] AND yr_built[new] THEN price[expensive]\n",
       " \tAND aggregation function : fmin\n",
       " \tOR aggregation function  : fmax,\n",
       " IF sqft_living[high] AND yr_built[new] THEN price[very expensive]\n",
       " \tAND aggregation function : fmin\n",
       " \tOR aggregation function  : fmax]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fuzzy_rules = [\n",
    "    rule11,\n",
    "    rule12,\n",
    "    rule13,\n",
    "    rule21,\n",
    "    rule22, \n",
    "    rule23,\n",
    "]\n",
    "\n",
    "price_cntrl = ctrl.ControlSystem(fuzzy_rules)\n",
    "\n",
    "sim = ctrl.ControlSystemSimulation(price_cntrl)\n",
    "\n",
    "fuzzy_rules"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Пример использования полученной нечеткой системы"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=============\n",
      " Antecedents \n",
      "=============\n",
      "Antecedent: sqft_living             = 1500\n",
      "  - low                             : 0.8173584905660377\n",
      "  - average                         : 0.18264150943396226\n",
      "  - high                            : 0.0\n",
      "Antecedent: yr_built                = 2016\n",
      "  - old                             : 0.0\n",
      "  - new                             : 0.8677551020408163\n",
      "\n",
      "=======\n",
      " Rules \n",
      "=======\n",
      "RULE #0:\n",
      "  IF sqft_living[low] AND yr_built[old] THEN price[very cheap]\n",
      "\tAND aggregation function : fmin\n",
      "\tOR aggregation function  : fmax\n",
      "\n",
      "  Aggregation (IF-clause):\n",
      "  - sqft_living[low]                                       : 0.8173584905660377\n",
      "  - yr_built[old]                                          : 0.0\n",
      "                        sqft_living[low] AND yr_built[old] = 0.0\n",
      "  Activation (THEN-clause):\n",
      "                                         price[very cheap] : 0.0\n",
      "\n",
      "RULE #1:\n",
      "  IF sqft_living[average] AND yr_built[old] THEN price[cheap]\n",
      "\tAND aggregation function : fmin\n",
      "\tOR aggregation function  : fmax\n",
      "\n",
      "  Aggregation (IF-clause):\n",
      "  - sqft_living[average]                                   : 0.18264150943396226\n",
      "  - yr_built[old]                                          : 0.0\n",
      "                    sqft_living[average] AND yr_built[old] = 0.0\n",
      "  Activation (THEN-clause):\n",
      "                                              price[cheap] : 0.0\n",
      "\n",
      "RULE #2:\n",
      "  IF sqft_living[high] AND yr_built[old] THEN price[average]\n",
      "\tAND aggregation function : fmin\n",
      "\tOR aggregation function  : fmax\n",
      "\n",
      "  Aggregation (IF-clause):\n",
      "  - sqft_living[high]                                      : 0.0\n",
      "  - yr_built[old]                                          : 0.0\n",
      "                       sqft_living[high] AND yr_built[old] = 0.0\n",
      "  Activation (THEN-clause):\n",
      "                                            price[average] : 0.0\n",
      "\n",
      "RULE #3:\n",
      "  IF sqft_living[low] AND yr_built[new] THEN price[average]\n",
      "\tAND aggregation function : fmin\n",
      "\tOR aggregation function  : fmax\n",
      "\n",
      "  Aggregation (IF-clause):\n",
      "  - sqft_living[low]                                       : 0.8173584905660377\n",
      "  - yr_built[new]                                          : 0.8677551020408163\n",
      "                        sqft_living[low] AND yr_built[new] = 0.8173584905660377\n",
      "  Activation (THEN-clause):\n",
      "                                            price[average] : 0.8173584905660377\n",
      "\n",
      "RULE #4:\n",
      "  IF sqft_living[average] AND yr_built[new] THEN price[expensive]\n",
      "\tAND aggregation function : fmin\n",
      "\tOR aggregation function  : fmax\n",
      "\n",
      "  Aggregation (IF-clause):\n",
      "  - sqft_living[average]                                   : 0.18264150943396226\n",
      "  - yr_built[new]                                          : 0.8677551020408163\n",
      "                    sqft_living[average] AND yr_built[new] = 0.18264150943396226\n",
      "  Activation (THEN-clause):\n",
      "                                          price[expensive] : 0.18264150943396226\n",
      "\n",
      "RULE #5:\n",
      "  IF sqft_living[high] AND yr_built[new] THEN price[very expensive]\n",
      "\tAND aggregation function : fmin\n",
      "\tOR aggregation function  : fmax\n",
      "\n",
      "  Aggregation (IF-clause):\n",
      "  - sqft_living[high]                                      : 0.0\n",
      "  - yr_built[new]                                          : 0.8677551020408163\n",
      "                       sqft_living[high] AND yr_built[new] = 0.0\n",
      "  Activation (THEN-clause):\n",
      "                                     price[very expensive] : 0.0\n",
      "\n",
      "\n",
      "==============================\n",
      " Intermediaries and Conquests \n",
      "==============================\n",
      "Consequent: price                    = 2057.986185801524\n",
      "  very cheap:\n",
      "    Accumulate using accumulation_max : 0.0\n",
      "  cheap:\n",
      "    Accumulate using accumulation_max : 0.0\n",
      "  average:\n",
      "    Accumulate using accumulation_max : 0.8173584905660377\n",
      "  expensive:\n",
      "    Accumulate using accumulation_max : 0.18264150943396226\n",
      "  very expensive:\n",
      "    Accumulate using accumulation_max : 0.0\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "np.float64(2057.986185801524)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Пример предсказания\n",
    "sim.input[\"yr_built\"] = 2016\n",
    "sim.input[\"sqft_living\"] = 1500\n",
    "sim.compute()\n",
    "sim.print_state()\n",
    "display(sim.output[\"price\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Функция для автоматизации вычисления целевой переменной Y на основе вектора признаков X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Функция предсказания\n",
    "def fuzzy_pred(row):\n",
    "    sim.input[\"yr_built\"] = row[\"yr_built\"]\n",
    "    sim.input[\"sqft_living\"] = row[\"sqft_living\"]\n",
    "    sim.compute()\n",
    "    return sim.output[\"price\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Тестирование нечеткой системы на обучающей выборке"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>sqft_living</th>\n",
       "      <th>sqft_lot</th>\n",
       "      <th>floors</th>\n",
       "      <th>waterfront</th>\n",
       "      <th>view</th>\n",
       "      <th>condition</th>\n",
       "      <th>grade</th>\n",
       "      <th>sqft_above</th>\n",
       "      <th>sqft_basement</th>\n",
       "      <th>yr_built</th>\n",
       "      <th>yr_renovated</th>\n",
       "      <th>sqft_living15</th>\n",
       "      <th>sqft_lot15</th>\n",
       "      <th>PricePred</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9848</th>\n",
       "      <td>450000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1610</td>\n",
       "      <td>6160</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>1610</td>\n",
       "      <td>0</td>\n",
       "      <td>1977</td>\n",
       "      <td>0</td>\n",
       "      <td>1750</td>\n",
       "      <td>6305</td>\n",
       "      <td>429.624194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3710</th>\n",
       "      <td>361000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1.50</td>\n",
       "      <td>1200</td>\n",
       "      <td>7236</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>1200</td>\n",
       "      <td>0</td>\n",
       "      <td>1975</td>\n",
       "      <td>0</td>\n",
       "      <td>1680</td>\n",
       "      <td>7800</td>\n",
       "      <td>428.207965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16998</th>\n",
       "      <td>413000.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1410</td>\n",
       "      <td>6000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>810</td>\n",
       "      <td>600</td>\n",
       "      <td>1925</td>\n",
       "      <td>0</td>\n",
       "      <td>1500</td>\n",
       "      <td>4800</td>\n",
       "      <td>310.375162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1968</th>\n",
       "      <td>490000.0</td>\n",
       "      <td>4</td>\n",
       "      <td>2.25</td>\n",
       "      <td>2020</td>\n",
       "      <td>85813</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>2020</td>\n",
       "      <td>0</td>\n",
       "      <td>1995</td>\n",
       "      <td>0</td>\n",
       "      <td>2120</td>\n",
       "      <td>85813</td>\n",
       "      <td>2752.148335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16407</th>\n",
       "      <td>333000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1050</td>\n",
       "      <td>7560</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>1050</td>\n",
       "      <td>0</td>\n",
       "      <td>1951</td>\n",
       "      <td>0</td>\n",
       "      <td>1490</td>\n",
       "      <td>7560</td>\n",
       "      <td>310.403184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17067</th>\n",
       "      <td>219950.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1.50</td>\n",
       "      <td>1210</td>\n",
       "      <td>5200</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>1210</td>\n",
       "      <td>0</td>\n",
       "      <td>1969</td>\n",
       "      <td>0</td>\n",
       "      <td>1120</td>\n",
       "      <td>5200</td>\n",
       "      <td>398.865957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2304</th>\n",
       "      <td>290000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1340</td>\n",
       "      <td>9840</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>1340</td>\n",
       "      <td>0</td>\n",
       "      <td>1949</td>\n",
       "      <td>0</td>\n",
       "      <td>1610</td>\n",
       "      <td>8949</td>\n",
       "      <td>327.464832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16319</th>\n",
       "      <td>500000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1440</td>\n",
       "      <td>7130</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>1210</td>\n",
       "      <td>230</td>\n",
       "      <td>1948</td>\n",
       "      <td>0</td>\n",
       "      <td>1970</td>\n",
       "      <td>7130</td>\n",
       "      <td>331.587824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13758</th>\n",
       "      <td>1712500.0</td>\n",
       "      <td>3</td>\n",
       "      <td>3.25</td>\n",
       "      <td>2940</td>\n",
       "      <td>5432</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>2440</td>\n",
       "      <td>500</td>\n",
       "      <td>1978</td>\n",
       "      <td>0</td>\n",
       "      <td>4400</td>\n",
       "      <td>5500</td>\n",
       "      <td>430.289457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>408</th>\n",
       "      <td>364950.0</td>\n",
       "      <td>4</td>\n",
       "      <td>2.50</td>\n",
       "      <td>1930</td>\n",
       "      <td>6957</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>1930</td>\n",
       "      <td>0</td>\n",
       "      <td>1995</td>\n",
       "      <td>0</td>\n",
       "      <td>2090</td>\n",
       "      <td>6996</td>\n",
       "      <td>2752.148335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19017</th>\n",
       "      <td>3800000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>4.25</td>\n",
       "      <td>5510</td>\n",
       "      <td>35000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>4910</td>\n",
       "      <td>600</td>\n",
       "      <td>1997</td>\n",
       "      <td>0</td>\n",
       "      <td>3430</td>\n",
       "      <td>45302</td>\n",
       "      <td>2769.489768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5736</th>\n",
       "      <td>600000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.50</td>\n",
       "      <td>2320</td>\n",
       "      <td>52272</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>2320</td>\n",
       "      <td>0</td>\n",
       "      <td>1974</td>\n",
       "      <td>0</td>\n",
       "      <td>2200</td>\n",
       "      <td>52272</td>\n",
       "      <td>427.457268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2362</th>\n",
       "      <td>278000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1.00</td>\n",
       "      <td>860</td>\n",
       "      <td>7632</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>860</td>\n",
       "      <td>0</td>\n",
       "      <td>1920</td>\n",
       "      <td>0</td>\n",
       "      <td>890</td>\n",
       "      <td>7632</td>\n",
       "      <td>270.308711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17316</th>\n",
       "      <td>578550.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.50</td>\n",
       "      <td>2120</td>\n",
       "      <td>6602</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>2120</td>\n",
       "      <td>0</td>\n",
       "      <td>1989</td>\n",
       "      <td>0</td>\n",
       "      <td>2330</td>\n",
       "      <td>7795</td>\n",
       "      <td>435.688044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1713</th>\n",
       "      <td>300000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1.75</td>\n",
       "      <td>1280</td>\n",
       "      <td>12776</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>1280</td>\n",
       "      <td>0</td>\n",
       "      <td>1977</td>\n",
       "      <td>0</td>\n",
       "      <td>1680</td>\n",
       "      <td>11704</td>\n",
       "      <td>429.624194</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           price  bedrooms  bathrooms  sqft_living  sqft_lot  floors  \\\n",
       "9848    450000.0         2       2.00         1610      6160     2.0   \n",
       "3710    361000.0         3       1.50         1200      7236     1.0   \n",
       "16998   413000.0         4       1.00         1410      6000     1.0   \n",
       "1968    490000.0         4       2.25         2020     85813     2.0   \n",
       "16407   333000.0         3       1.00         1050      7560     1.0   \n",
       "17067   219950.0         3       1.50         1210      5200     1.0   \n",
       "2304    290000.0         2       1.00         1340      9840     1.0   \n",
       "16319   500000.0         2       1.00         1440      7130     1.0   \n",
       "13758  1712500.0         3       3.25         2940      5432     3.0   \n",
       "408     364950.0         4       2.50         1930      6957     2.0   \n",
       "19017  3800000.0         3       4.25         5510     35000     2.0   \n",
       "5736    600000.0         3       2.50         2320     52272     1.5   \n",
       "2362    278000.0         3       1.00          860      7632     1.0   \n",
       "17316   578550.0         3       2.50         2120      6602     2.0   \n",
       "1713    300000.0         3       1.75         1280     12776     1.0   \n",
       "\n",
       "       waterfront  view  condition  grade  sqft_above  sqft_basement  \\\n",
       "9848            0     0          4      8        1610              0   \n",
       "3710            0     0          3      7        1200              0   \n",
       "16998           0     0          3      7         810            600   \n",
       "1968            0     0          3      7        2020              0   \n",
       "16407           0     0          3      7        1050              0   \n",
       "17067           0     0          5      6        1210              0   \n",
       "2304            0     0          3      7        1340              0   \n",
       "16319           0     2          3      7        1210            230   \n",
       "13758           0     3          4     10        2440            500   \n",
       "408             0     0          3      8        1930              0   \n",
       "19017           0     4          3     13        4910            600   \n",
       "5736            0     0          3      8        2320              0   \n",
       "2362            0     0          3      6         860              0   \n",
       "17316           0     0          4      8        2120              0   \n",
       "1713            0     0          4      7        1280              0   \n",
       "\n",
       "       yr_built  yr_renovated  sqft_living15  sqft_lot15    PricePred  \n",
       "9848       1977             0           1750        6305   429.624194  \n",
       "3710       1975             0           1680        7800   428.207965  \n",
       "16998      1925             0           1500        4800   310.375162  \n",
       "1968       1995             0           2120       85813  2752.148335  \n",
       "16407      1951             0           1490        7560   310.403184  \n",
       "17067      1969             0           1120        5200   398.865957  \n",
       "2304       1949             0           1610        8949   327.464832  \n",
       "16319      1948             0           1970        7130   331.587824  \n",
       "13758      1978             0           4400        5500   430.289457  \n",
       "408        1995             0           2090        6996  2752.148335  \n",
       "19017      1997             0           3430       45302  2769.489768  \n",
       "5736       1974             0           2200       52272   427.457268  \n",
       "2362       1920             0            890        7632   270.308711  \n",
       "17316      1989             0           2330        7795   435.688044  \n",
       "1713       1977             0           1680       11704   429.624194  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Применение к обучающим данным\n",
    "result_train = houses_train.copy()\n",
    "result_train[\"PricePred\"] = result_train.apply(fuzzy_pred, axis=1)\n",
    "\n",
    "result_train.head(15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Тестирование нечеткой системы на тестовой выборке"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>sqft_living</th>\n",
       "      <th>sqft_lot</th>\n",
       "      <th>floors</th>\n",
       "      <th>waterfront</th>\n",
       "      <th>view</th>\n",
       "      <th>condition</th>\n",
       "      <th>grade</th>\n",
       "      <th>sqft_above</th>\n",
       "      <th>sqft_basement</th>\n",
       "      <th>yr_built</th>\n",
       "      <th>yr_renovated</th>\n",
       "      <th>sqft_living15</th>\n",
       "      <th>sqft_lot15</th>\n",
       "      <th>PricePred</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8943</th>\n",
       "      <td>852600.0</td>\n",
       "      <td>4</td>\n",
       "      <td>2.50</td>\n",
       "      <td>3320</td>\n",
       "      <td>11901</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>2650</td>\n",
       "      <td>670</td>\n",
       "      <td>1977</td>\n",
       "      <td>0</td>\n",
       "      <td>2700</td>\n",
       "      <td>11114</td>\n",
       "      <td>429.624194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16711</th>\n",
       "      <td>795000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.75</td>\n",
       "      <td>1820</td>\n",
       "      <td>7517</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>1820</td>\n",
       "      <td>0</td>\n",
       "      <td>1997</td>\n",
       "      <td>0</td>\n",
       "      <td>2540</td>\n",
       "      <td>8035</td>\n",
       "      <td>2769.489768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4156</th>\n",
       "      <td>222000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1.75</td>\n",
       "      <td>1240</td>\n",
       "      <td>7560</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>1070</td>\n",
       "      <td>170</td>\n",
       "      <td>1967</td>\n",
       "      <td>0</td>\n",
       "      <td>1650</td>\n",
       "      <td>7560</td>\n",
       "      <td>390.429374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20873</th>\n",
       "      <td>375000.0</td>\n",
       "      <td>6</td>\n",
       "      <td>2.25</td>\n",
       "      <td>3206</td>\n",
       "      <td>5793</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>3206</td>\n",
       "      <td>0</td>\n",
       "      <td>2012</td>\n",
       "      <td>0</td>\n",
       "      <td>2527</td>\n",
       "      <td>5804</td>\n",
       "      <td>2579.862557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6498</th>\n",
       "      <td>274950.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.25</td>\n",
       "      <td>1570</td>\n",
       "      <td>8767</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>1180</td>\n",
       "      <td>390</td>\n",
       "      <td>1990</td>\n",
       "      <td>0</td>\n",
       "      <td>1570</td>\n",
       "      <td>7434</td>\n",
       "      <td>436.002078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21109</th>\n",
       "      <td>315000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2.25</td>\n",
       "      <td>1240</td>\n",
       "      <td>705</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>1150</td>\n",
       "      <td>90</td>\n",
       "      <td>2009</td>\n",
       "      <td>0</td>\n",
       "      <td>1240</td>\n",
       "      <td>750</td>\n",
       "      <td>2085.193978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13160</th>\n",
       "      <td>240000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1320</td>\n",
       "      <td>24319</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>1320</td>\n",
       "      <td>0</td>\n",
       "      <td>1966</td>\n",
       "      <td>0</td>\n",
       "      <td>1430</td>\n",
       "      <td>98445</td>\n",
       "      <td>391.519006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3892</th>\n",
       "      <td>695500.0</td>\n",
       "      <td>5</td>\n",
       "      <td>2.75</td>\n",
       "      <td>2510</td>\n",
       "      <td>9180</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>1600</td>\n",
       "      <td>910</td>\n",
       "      <td>1975</td>\n",
       "      <td>0</td>\n",
       "      <td>2650</td>\n",
       "      <td>9780</td>\n",
       "      <td>428.207965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15547</th>\n",
       "      <td>910000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2700</td>\n",
       "      <td>6120</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>1350</td>\n",
       "      <td>1350</td>\n",
       "      <td>1962</td>\n",
       "      <td>0</td>\n",
       "      <td>1700</td>\n",
       "      <td>4590</td>\n",
       "      <td>416.400374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11770</th>\n",
       "      <td>1850000.0</td>\n",
       "      <td>5</td>\n",
       "      <td>3.25</td>\n",
       "      <td>3680</td>\n",
       "      <td>6060</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>2630</td>\n",
       "      <td>1050</td>\n",
       "      <td>1925</td>\n",
       "      <td>0</td>\n",
       "      <td>3050</td>\n",
       "      <td>5850</td>\n",
       "      <td>407.777504</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4323 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           price  bedrooms  bathrooms  sqft_living  sqft_lot  floors  \\\n",
       "8943    852600.0         4       2.50         3320     11901     2.0   \n",
       "16711   795000.0         3       2.75         1820      7517     1.0   \n",
       "4156    222000.0         3       1.75         1240      7560     1.0   \n",
       "20873   375000.0         6       2.25         3206      5793     2.0   \n",
       "6498    274950.0         3       2.25         1570      8767     1.0   \n",
       "...          ...       ...        ...          ...       ...     ...   \n",
       "21109   315000.0         2       2.25         1240       705     2.0   \n",
       "13160   240000.0         2       1.00         1320     24319     1.0   \n",
       "3892    695500.0         5       2.75         2510      9180     1.0   \n",
       "15547   910000.0         3       2.00         2700      6120     1.0   \n",
       "11770  1850000.0         5       3.25         3680      6060     2.0   \n",
       "\n",
       "       waterfront  view  condition  grade  sqft_above  sqft_basement  \\\n",
       "8943            0     0          5      9        2650            670   \n",
       "16711           0     0          3      9        1820              0   \n",
       "4156            0     0          3      8        1070            170   \n",
       "20873           0     0          3      7        3206              0   \n",
       "6498            0     0          3      7        1180            390   \n",
       "...           ...   ...        ...    ...         ...            ...   \n",
       "21109           0     0          3      7        1150             90   \n",
       "13160           0     0          3      7        1320              0   \n",
       "3892            0     1          4      8        1600            910   \n",
       "15547           0     0          4      8        1350           1350   \n",
       "11770           0     0          5      9        2630           1050   \n",
       "\n",
       "       yr_built  yr_renovated  sqft_living15  sqft_lot15    PricePred  \n",
       "8943       1977             0           2700       11114   429.624194  \n",
       "16711      1997             0           2540        8035  2769.489768  \n",
       "4156       1967             0           1650        7560   390.429374  \n",
       "20873      2012             0           2527        5804  2579.862557  \n",
       "6498       1990             0           1570        7434   436.002078  \n",
       "...         ...           ...            ...         ...          ...  \n",
       "21109      2009             0           1240         750  2085.193978  \n",
       "13160      1966             0           1430       98445   391.519006  \n",
       "3892       1975             0           2650        9780   428.207965  \n",
       "15547      1962             0           1700        4590   416.400374  \n",
       "11770      1925             0           3050        5850   407.777504  \n",
       "\n",
       "[4323 rows x 17 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "result_test = houses_test.copy()\n",
    "result_test[\"PricePred\"] = result_test.apply(fuzzy_pred, axis=1)\n",
    "result_test[\"PricePred\"] = result_test[\"PricePred\"].apply(lambda x: x)\n",
    "\n",
    "display(result_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Оценка результатов на основе метрик для задачи регрессии"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'RMSE_train': 649737.8258173236,\n",
       " 'RMSE_test': 649737.8258173236,\n",
       " 'RMAE_test': 736.5342738871541,\n",
       " 'R2_test': -2.298493549806802}"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import math\n",
    "from sklearn import metrics\n",
    "\n",
    "\n",
    "rmetrics = {}\n",
    "\n",
    "rmetrics[\"RMSE_train\"] = math.sqrt(\n",
    "    metrics.mean_squared_error(result_test[\"price\"], result_test[\"PricePred\"])\n",
    ")\n",
    "rmetrics[\"RMSE_test\"] = math.sqrt(\n",
    "    metrics.mean_squared_error(result_test[\"price\"], result_test[\"PricePred\"])\n",
    ")\n",
    "rmetrics[\"RMAE_test\"] = math.sqrt(\n",
    "    metrics.mean_absolute_error(result_test[\"price\"], result_test[\"PricePred\"])\n",
    ")\n",
    "rmetrics[\"R2_test\"] = metrics.r2_score(\n",
    "    result_test[\"price\"], result_test[\"PricePred\"]\n",
    ")\n",
    "\n",
    "rmetrics"
   ]
  }
 ],
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